\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"2663",leadTitle:null,fullTitle:"Child Abuse and Neglect - A Multidimensional Approach",title:"Child Abuse and Neglect",subtitle:"A Multidimensional Approach",reviewType:"peer-reviewed",abstract:"Child maltreatment constitutes a social problem that affects all societies of the world. 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Habib",coverURL:"https://cdn.intechopen.com/books/images_new/3598.jpg",editedByType:"Edited by",editors:[{id:"80821",title:"Dr.",name:"Maki K.",surname:"Habib",slug:"maki-k.-habib",fullName:"Maki K. 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It consists of assigning to each instance a class chosen from a set of predefined classes, according to the value of certain predictive attributes [1]. Its problem is to correctly classify an instance with indeterminate class. This classification can be done by several methods that are divided into two categories. The first category is based on the use of a model or a classifier such as decision trees and classification rules. However, the second category is based on the internal functioning of the learning algorithm such as neural networks [2] and support vector machines (SVMs). All these methods use large datasets to extract knowledge.
\nThe used datasets are organized in the form of tables. The tables’ columns are called the attributes, and they represent the characteristics of the dataset. Traditionally, the last attribute is called a class attribute. The tables’ rows represent the data, and they are called instances. The number of instances varies from one class to another. So, the number of instances of one class is larger than that of the second class in some existing datasets. Therefore, datasets are divided into two categories: balanced and imbalanced datasets. In the latter, instances are divided into two sets: majority instances which are the most frequent and minority instances which are the less frequent.
\nRule-based classification algorithms have a bias toward majority classes [3]. They tend to discover the rules with high values of accuracy and coverage. These rules are usually specific to majority instances, whereas specific rules that predict minority instances are usually ignored or treated as noise. Consequently, minority instances are often misclassified. Generally, because most classifiers are designed to minimize the global error rate [4], many problems occur. First, they perform poorly on imbalanced datasets, and they either produce general rules or very specific ones. In the first case, the classifier has a bias toward majority instances, and it ignores the minority ones. In the second case, the classifiers tend to overfit the training data which provokes poor classification accuracy on unseen data. Next, the cost of misclassifying a minority instance is usually more expensive than misclassifying a majority one [5, 6]. Finally, in many applications misclassifying a rare event can result in more serious problems than a common event [7]. For example, in case of cancerous cell detection in medical diagnosis, misclassifying non-cancerous cells may lead to some additional clinical tests, but misclassifying cancerous cells leads to very serious health risks.
\nThe class imbalance problem is a fundamental problem in machine learning, and it has received much attention [8, 9, 10, 11, 12, 13, 14]. This problem is known as partial classification [15], nugget discovery [16], classification problem with imbalanced datasets [17], or datasets with rare classes [18]. Considering the importance of this issue, a large amount of techniques have been developed trying to address this problem. These proposals can be divided into three groups which depend on how they deal with class imbalance. First, the algorithm-level approaches can either propose specific algorithms or modify the existing ones. Second, the data-level techniques introduce an additional processing step to decrease the effect of skewed class distribution such as undersampling and oversampling methods. Finally, the hybrid-level methods combine algorithm level and data level such as boosting and cost-sensitive learning.
\nThis chapter is organized as follows. Section 2 presents the classification problem in imbalanced datasets. In Section 3, we present some domains in which the datasets appear. In Section 4, we present the evaluation metrics used in classification problem in imbalanced datasets. In Section 5, we detail the different approaches and techniques used to handle classification in imbalanced datasets. Finally, in Section 6, we make our concluding remarks.
\nIn the binary imbalanced datasets, the number of instances of one class is higher than that of the second class. Consequently, the first class is known as majority class and the second class as minority one. Therefore, this dataset contains two kinds of instances: majority and minority.
\nThe distribution of instances in imbalanced binary datasets is measured by the imbalanced ratio (IR) [19] which is defined in Eq. (1):
\nAccording to the value of IR, the imbalanced datasets are divided into three classes [20]: datasets with low imbalance (IR is between 1.5 and 3), datasets with medium imbalance (IR is between 3 and 9), and datasets with high imbalance (IR is higher than 9).
\nThe imbalanced datasets appear in the following several domains.
\nEvery year, the telecommunication industry suffers billions of dollars in unrecoverable debts. Therefore, uncollectible control is a major problem in the industry. One solution is to use large amounts of historical data to build models that are used to assess risk for each customer client or for each transaction to support risk management that reduces the level of unrecoverable debt. However, in a dataset, nonpayment of customers includes a few percent of the population [21].
\nClinical datasets store large amounts of patient information. Data mining technique is applied on these datasets to uncover the relationships and trends between clinical and pathological data. It aims to understand the evolution and characteristics of certain diseases. However, in these datasets, cases of disease are rarer than the normal population [22].
\nNetwork-based computer systems are increasingly playing a vital role in modern societies. Attacks on computer systems and networks are growing. Different categories of network attacks exist; some are numerous, and others are rare. For example, the KDD-CUP’99 dataset contains four categories of network attacks: denial of service (DoS), monitoring (probe), root to local (R2L), and user to root. (U2R). The last two attacks are intrinsically rare [23].
\nThe classical performance measures used for evaluating the performances of classifiers when used with balanced datasets are not appropriate for imbalanced datasets. This is because they have a strong bias toward majority class and are sensitive to class skews [24, 25, 26, 27]. For example, the accuracy measure is not appropriate for the problem of imbalanced datasets [28]. If we consider a dataset which contains only 1% of minority instances and 99% of majority instances, the accuracy is 99% if all majority instances are well classified. However, misclassified 1% minority instances may lead to an enormous cost, and 99% accuracy could be a disaster for a medical diagnosis. Consequently, other metrics are necessary for measuring the performances of classifiers.
\nSome measures are extracted directly from the confusion matrix. They measure the classification performance of the majority and minority classes independently. Some others are combined to measure the performance of a classifier. They are described below.
\nIt is a measure of accuracy [29]. It represents the percentage of well-classified minority instances in relation to all instances whose predicted class is a minority. It is defined in Eq. (2):
\nIt is the percentage of minority instances which are well classified as belonging to the minority class. In literature, this metric has several names such as sensitivity, true positive rate (TPrate), or positive accuracy [30]. It is defined in Eq. (3):
\nIt is the percentage of majority instances which are well classified as belonging to the majority class. This measure is also known as true negative rate (TNrate) or negative accuracy. It is defined in Eq. (4):
\nIt is the percentage of majority instances misclassified as belonging to the minority class. It is defined in Eq. (5):
\nIt is the percentage of minority instances misclassified as belonging to the majority class. It is defined in Eq. (6):
\nIt indicates the balance between classification performances on the majority and minority classes [30]. A poor performance in the prediction of the positive instances will lead to a low G-mean value even if the negative instances are correctly classified by the model [31]. It has been used by several researchers for evaluating classifiers on imbalanced datasets [31, 32, 33]. G-Mean takes recall and specificity into account simultaneously. It is defined in Eq. (7). This metric will be used to test our approach:
\nIt is defined as the harmonic mean of precision and recall [34]. Its value increases proportionally with the increase of precision and recall; a high value of F-measure indicates that the model performs better on the minority class. This metric is defined in Eq. (8):
\nThe ROC curve [34, 35] is a technique for visualization, organization, and selection of classifiers based on their performances. It has long been used in signal detection to represent the trade-off between the success rate and false alarm rate of classifiers. It is a two-dimensional graph where TPrate is plotted on the y-axis and FPrate is plotted on the x-axis.
\nFor a discrete classifier, the pair (FPrate, TPrate) is produced that corresponds to one point in the ROC space. However, a probabilistic classifier produces a continuous numerical value. Therefore, a threshold may be used to produce a series of points in the ROC space to produce a curve instead of one point.
\nFrom the ROC curve, we define another measure called area under the curve (AUC) [35, 36] defined in Eq. (9) to compare the performance of two classifiers. If the area associated with classifier C1 is greater than that associated with classifier C2, then the performances of C1 are better than C2:
\nThe several approaches have been proposed to handle the classification problem in imbalanced datasets. These approaches are divided into three levels [20]: data level, algorithm level, and hybrid level.
\nIt consists of resampling the data in order to decrease the effect caused by the imbalance [3]. They are classified into three groups [3]: oversampling, undersampling, and hybrid methods.
\nOversampling is used to increase the size of an imbalanced dataset by duplicating some minority instances. This duplication can be done by the following methods.
\nIt duplicates some minority instances chosen randomly [3]. Therefore, the multiple copies of minority instances increase the overlapping between these instances [37]. In particular, the overlapping appears when the produced classifier contains more specific rules for multiple copies of the same instance. As a result, the accuracy of learning is high in this scenario, and the performance of the classifier for the test is generally low [38].
\nSMOTE [39] is a synthetic method with data generation. It has achieved several successes in various fields [3]. It creates a synthetic example xnew for each minority instance xi as follows. It determines the K-nearest neighbors (which are minority instances whose Euclidean distance between them and xi is the smallest) of xi. Then, it selects randomly one of K-nearest neighbors yi. Finally, it applies Eq. (10), where δ is a random number ∈ [0, 1]. Therefore, we understand that xnew is a point of the segment joining xi and yi:
\nSMOTE will not ignore the minority instances because it generalizes decision regions for them. But SMOTE has two problems [40]: overgeneralization and variance. The first problem is due to the blind generalization of the minority area without taking into account the majority class, which increases the number of overlapping between classes. The second problem concerns the number of generated synthetic instances which is set in advance without taking into account the IR.
\nSMOTE does not consider the distribution of minority instances and those that are noisy in a dataset. For this reason, MSMOTE [41] divides the minority instances into three groups: security, border, and latent noises.
\nAn instance is secretary, if the number of its K-nearest neighbors belonging to the minority class is greater than those belonging to the majority class.
\nAn instance is border, if the number of its K-nearest neighbors belonging to the minority class is lower than those belonging to the majority class.
\nAn instance is latent noise, if all its K-nearest neighbors have the majority class.
\nMSMOTE generates synthetic instances for all security instances in the same way as SMOTE. However, for each border instance, it selects the most nearest neighbor to generate a synthetic example. But, it does not generate synthetic instances for noisy instances, because they decrease the classifier’s performances.
\nBorder instances and those nearby are more likely to be misclassified than those that are far from the border, and they are the most important for classification. Based on this analysis, the border instances contribute little in the classification. Therefore, the Borderline-SMOTE [42] method has been proposed to apply oversampling to border minority instances instead of applying it to all minority instances. To do this, it constructs a set of border minority instances known as DANGER. Then, it applies SMOTE for each instance of the DANGER set.
\nADASYN [43] uses a function called density as an automatic criterion to take a decision about the number of synthetic instances that may be generated of each minority instance.
\nIt consists of reducing the data size by deleting some majority instances with the objective of equalizing the number of instances of each class [44]. There are several approaches of undersampling that differ in the way of selection of majority instances that will be deleted.
\nRUS [4, 44] removes some majority instances selected randomly. But it can potentially hinder learning [37, 38, 45]; the deleted majority instances can cause the classifier to ignore important concepts related to the majority class.
\nIt is proposed to avoid the loss of information caused by RUS [46]. Among the algorithms of this kind of undersampling, we have the following.
\nIt aims to a better exploitation of majority instances ignored by RUS. At first, it divides the training dataset into minority set P and majority set N of sizes n and p, respectively [46]. Then, it builds T subsets N1, N2…, NT of size p by applying random sampling with replacement on N. After that, it generates T classifiers H1, H2…, HT. The classifier Hi is produced by applying AdaBoost on Ni and P, and it contains the concepts of all majority and minority instances. Finally, it constructs the final classifier H by combining the T generated classifiers.
\nThe training dataset is composed of the sets P of minority instances of size p and N of majority instances of size n [46]. BalanceCascade constructs at each iteration the classifier Hi from all the set P and the subset E chosen randomly from N, with |E| = p. Then, it updates N by deleting all majority instances which are well classified by Hi. This algorithm explores the majority instances in a supervised way because the set of majority instances is updated after generation of each classifier.
\nThis technique [44] is based on the distribution characteristics of data by applying KNN algorithm [47]. The following three methods of this technique have been proposed:
\nNearMiss-1 selects majority instances as follows:
For each majority instance xi.
For each minority instance xj: Computes the distance dij between xi and xj.
Identify the three nearest neighbors xk (1 ≤ k ≤ 3) for xi that represents minority instances.
Compute the average distance di defined in Eq. (11):
\n
Select majority instances xi whose average distance to the three closest minority class instances is the smallest.
NearMiss-2 method has the same steps as the previous method. But, it selects the majority instances whose average distance to the three farthest minority class instances is the smallest.
\nNearMiss-3 selects a given number of the closest majority instances for each minority instance to guarantee that every minority instance is surrounded by some majority instances.
\nThe data cleaning techniques were applied to eliminate the overlapping between classes. In the following four subsections, we represent some methods.
\nTomek links method [48] may be used as an undersampling method. It deletes the noisy majority instances and those that are close to the border. The obtained training dataset after removing the Tomek links is organized into set of clusters. This method may be used as a data cleaning technique to delete majority and minority instances.
\nCNN [49] is an instance reduction algorithm proposed by Hart. It deletes the redundant majority instances. An instance is considered as redundant if it can be deduced from other instances. CNN uses the initial training dataset E to construct the consistent dataset E’ that contains instances that correctly classify all instances of E using 1-NN algorithm. Its steps are:
Copy the first majority instance x and all the minority instances of the training dataset E into the sub dataset E’.
While there are misclassified instances in E, do:
Classify the instance y (belonging to E) using E’ and 1-NN.
Add y to E’, if it is misclassified.
CNN is sensitive to noise. However, noisy instances are more susceptible to be misclassified [50], and they will misclassify the instances of test dataset [50, 51].
\nNCL is an undersampling technique introduced by Laurikkala [52] to balance a dataset by applying data reduction. Its main advantage is that it takes into account the quality of the data with a focus on data cleaning more than reduction. It removes noisy majority instances using the edited nearest neighbor (ENN) algorithm, which is an instance reduction algorithm developed by Wilson [53]. It is used to delete all instances whose class differs at least twice from the class of its three nearest neighbors.
\nOSS [17] is the result of using CNN followed by Tomek links. CNN is applied to remove redundant majority instances. However, Tomek links deletes the noisy majority instances and border minority instances.
\nEUS [20] results from the application of prototype selection [54] and genetic algorithm. It has eight models that depend on the objective that EUS aims to reach. For the first objective, there are two purposes. The first one is to balance a dataset without losing the accuracy, and then EUS is known as evolutionary balancing undersampling (EBUS). In the second one, EUS aims to obtain an optimal power of classification without taking in the consideration the balance of a dataset; it is called evolutionary undersampling guided by classification measures (EUSCM). For the second objective, there are two possibilities: majority selection (MS) instances only or global selection (GS) of both majority and minority instances.
\nThese methods combine undersampling and oversampling. They aim to eliminate the overfitting [3] caused by oversampling methods. For examples, SMOTE+Tomek links [17] applies Tomek links after generation of synthetic minority instances by SMOTE, and SMOTE+ENN [17] uses ENN to delete minority and majority instances. For this, each misclassified instance of training dataset by its three nearest neighbors is deleted.
\nMost approaches are based on either modifying the existing complete classification algorithms in order to adapt them to the imbalanced datasets or proposing specific ones.
\nA decision tree [55, 56, 57, 58] is the most popular form of rule-based classifiers. It allows to model simply, graphically, and quickly a phenomenon more or less complex. Its readability, speed of execution, and the few necessary hypotheses a priori explain its current popularity. All the methods of constructing a decision tree have these operators: deciding if a node is terminal, selection of a test to associate to a node, and assignation a class to a leaf.
\nThe existing methods of construction the decision trees differ by the choices made for different operators. CART [59] and C4.5 [60] are the most popular algorithms for decision trees.
\nIn the construction phase of a tree, C4.5 selects the node attribute that maximizes the information gain [60], that is, a high value of confidence. However, this measure is not suitable for imbalanced datasets because the most confident rules do not imply that they are the most significant, and some of the most significant rules may not be the most confident (may not have high confidence). The same problem arises for CART, which uses the Gini function [60]. These algorithms focus on the antecedent to find the class. Also, they use sensitive measures to the class distribution. For these reasons, some approaches have been proposed which apply nonsensitive measures [61] or modify the construction phase.
\nFor example,
However, obtaining rules which have a high CC value is still insufficient to solve the problem. So, it is necessary to make sure that the classes implied by these rules not only have great confidence but are more interesting than their alternative classes. As a result, the new class confidence proportion (CCP) measure has been proposed. It is defined in Eq. (13):
\nTherefore, the CCPDT approach modifies the C4.5 algorithm by replacing the entropy (the attribute partition criterion) by CCP.
\nThe Kernel-based learning methods are inspired by the statistical theory of learning and the dimensions of Vapnik-Chervonenkis (VC) [63], such as support vector machines (SVMs). These latter are supervised learning methods. They are used for classification in binary datasets in order to find a classifier that separates the data and maximizes the distance between these two classes. This classifier is linear, and it is called hyperplane. SVMs aim to find the most optimal hyperplane, which passes in the middle of the points of two classes and that maximizes the margin in order to minimize the classification error [64].
\nIn imbalanced datasets, the ideal hyperplane is close to the majority instances, and the decision boundary is very close to the minority instances. In this case, the support vectors representing the minority instances are far from the ideal hyperplane. Thus, their contribution to the final hypothesis is little [32, 65, 66]. To solve this problem, several methods have been proposed that differ from the mechanism used. For example, in [67] the following three approaches were presented:
Boundary movement (BM) that modifies the coefficient b in the kernel function.
Biased penalties (BP) introduce different penalty factors for the minority and majority classes in objective function in the Lagrangian formulation. These factors reflect the importance of classes during the learning phase.
Border class alignment (BCA) expands the border around the minority class much more than the border around the majority class.
The specific algorithms have been proposed to deal with classification problem in imbalanced datasets. Among them, we present RLSD and LUPC.
\nRLSD [62] is an efficient algorithm for handling imbalanced datasets. Its discovery process leads from a specific search to a general search. First, it discovers rules for minority instances used in learning. Then, it compares them with majority instances. It has the following three research phases:
Discretization phase consists of dividing the values of a numerical attribute into a small number of intervals. Each interval is mapped by a discrete symbol.
Rule generation phase is a frequent data discovery process for minority instances. This algorithm summarizes this phase:
Input: the set of minority instances P and the maximum number of allowed rules Output: the set of rules: Rules. Begin 1. Initially, the rule set is empty : Rules :=Ø; 2. for each minority instance pϵP do 2.1. The rule R:=p; 2.2. Consider R as an initial rule; 2.3. If R does not belong to Rules then 2.3.1. Merge R: For each rule RE ϵ Rules: If R and RE have common conditions then generate the new rule NR with common conditions and apply the procedure Add_Rules to add NR to Rules. 2.3.2. Apply the procedure Add_Rules to add R to Rules. 3. for each rule RE ϵ Rules do if TPrate(RE)<Min_TPrate then delete RE End. | \n
The procedure Add_Rules adds the concerned rule R to the set of rules if it does not belong to this set. After that, if the number of rules exceeds M, then it deletes a rule selected randomly.
\n3. The evaluation and rule selection phase: in rule evaluation step, RLSD calculates the accuracy of each generated rule by the correspondence with each majority instance. A rule is deleted if its precision (defined in Eq. (3)) is less than the minimum precision. In rule selection step, RLSD selects the rule with the highest F-measure value (defined in Eq. (8)). Then, it deletes all minority instances covered by this rule. After that, F-measure is recalculated for the remaining rules using the rest of minority instances. This process is repeated until there are no more minority instances or there is no rule that covers the remaining minority instances.
\nThe main feature of LUPC [67] is the combination of the separate and conquer rule induction method [68] and the association rules [69].
\nIt lets an imbalanced dataset of size D composed of the set of positive (minority) instances Pos and the set of negative (majority) instances Neg.
\nLUPC uses three measures of performances: accuracy [70] (acc), error rate (err) [64], and positive cover ratio (PCR). They are defined in Eqs. (14)–(16):
\nwhere the coverage (Cov) [70] of the rule R is the percentage of instances that are covered by this rule. It is defined in Eq. (17):
\nCov+(R) is the number of covered instances that have the same class as that of R.
\nA rule is
A rule is
The steps of LUPC are:
\n\n and the minimum PCR: min_cov \n \n Rules_sets:=Ø ; α, β := while (Pos ≠ Ø and (α, β)≠(min_acc, min_cov)) Rule R := Best rule \n Pos := Pos – { instances covered by R } Rules_sets := Rules_sets U R ; \n \n Rules_sets:=Post traitement(Rules_sets) ; \n | \n
The procedure “Initialize” depends on the user-specified bias on PCR or accuracy. It initializes α and β as min_acc and min_cov, respectively. Otherwise, α is initialized to 0.95 or min_acc if min_acc is greater than 0.95, and β is initialized to the maximum value of PCR of the attribute-value pairs available on the minority instances. To find the best rule, LUPC follows these steps:
Construct the set of attribute-value pairs: build the set E1 of all pairs, where each available pair (attribute, value) for positive instances is considered as a part of condition whose class is C+. Choose the set E2 (is a subset of E1) of the candidate pairs: each pair belonging to E1 is considered as a candidate pair if it covers more than α * β * |D| minority instances. Identify the αβ-strong pairs: each candidate pair will be checked on the Neg instances to see if it is αβ-strong. Order the αβ-strong pairs either by their precision or by their PCR. Choose η attribute-value pair candidates which are αβ-strong, and add them to the set of attribute-value pairs. In the case where the number of αβ-strong pairs < η, then add the pairs that are not αβ-strong and that have either a high accuracy or a high PCR.
Generate the set of candidate rules that contains γ rules belonging to the set of attribute-value pairs as follows:
Order all attribute-value pairs according to the accuracy and/or PCR.
If the number of αβ-strong pairs is greater than or equal to γ, then add γ pairs to the set of candidate rules otherwise:
First, put the αβ-strong pairs and the non-αβ-strong pairs in the set of candidate rules. Then, delete non-αβ-strong rules whose PCR is lower than β. After that, improve the set of candidate rules by iteratively executing the following procedure:
Generate new rules by combining each non-αβ-strong rule of the set of candidate rules with the pairs which are in the set of attribute-value pairs.
If the generated rules become αβ-strong, then they will be inserted in the first part of the set of candidate rules.
The procedure stops if there is no change in the non-αβ-strong rules or the number of rules in the set of candidate rules is greater than γ.
Reject the rules that satisfy the condition given in Eq. (20).
The values of α and β are gradually reduced by the rate Δa and Δc, respectively. The default quantities used are Δa = 2% and Δc = 1%.
\nSome methods of the complete classification cannot deal with the classification in imbalanced datasets without being combined with other techniques. Among these methods, we present ensemble methods, the cost-sensitive learning, and some other approaches based on metheuristics.
\nEnsemble methods build a series of N classifiers and combine them to produce the final classifier C* using voting strategies. They aim to obtain a high precision classifier. They are divided into two classes: boosting and bagging.
\nBoosting algorithms [64] focus on difficult instances to classify without differentiating their classes. According to Prof. Zhou Zhi-Hua [71], the boosting algorithms are very efficient and able to deal with classification in imbalanced datasets, because the minority instances are likely to be misclassified, and therefore, they will have weights higher in the following iterations.
\nHowever, Mikel G. et al. [72] considered that the integration of data sampling methods can reduce the additional costs of automatically detecting the optimal distribution of representative classes and samples and also reduce the bias of a specific learning algorithm. Among these methods, we have SMOTEBoost, RUSBoost, and DataBoost-IM.
\nIt alters the distribution of the training dataset by adding minority instances generated by SMOTE [73] in order to provide it to the algorithm AdaBoost.M2 [74].
\nIt operates in a similar way to SMOTEBoost, but it applies random undersampling on the training dataset [75].
\nIt combines AdaBoost.M1 [76] with a data generation strategy [31]. It differs from the two previous algorithms, because it performs the balancing process for majority and minority instances after identifying the difficult instances. Its steps are as follows:
Produce the classifier C to detect the misclassified instances which are called seeds.
Order the seeds in ascending order of their weight.
Construct the sets MAJ and MIN, which contain Mj majority instances and Mm minority instances, respectively, that have the highest weights.
Generate N synthetic instances for each majority seed and M synthetic instances for each minority seed.
Update the weights taking into consideration the newly added synthetic instances.
It constructs N classifiers on N distinct datasets [77]. Each dataset is known as bag; it is obtained by random sampling with replacement.
\nIn imbalanced datasets, the number of majority instances in a bag is also high. The main factor to apply on bagging to adapt it to this kind of datasets is the way of collecting the instances. We distinguish three main algorithms in this family:
\nThe distribution of instances may be taken into consideration in order to equalize the number of minority instances Nmin and the number of majority instances Nmaj [78]. Instead of constructing the bags randomly, we apply the oversampling according to the following two possibilities. In the first one, the minority instances are duplicated by oversampling, and majority instances are added directly, or they are selected by random sampling with replacement to increase the diversity. In the second one, the SMOTEBagging [78] is applied, where A%*Nmaj minority instances are selected by random drawing with replacement and the remaining instances are generated by SMOTE. The factor A is called resampling rate. It is equal to 10% in the first iteration and 100% in the last (it is multiple of 10).
\nThe number of majority instances is reduced to the number of minority instances in each bag [79]. All minority instances may be in the same bag. But for increasing the diversity, they can be selected by random sampling with replacement.
\nIt follows the two previous methodologies, but it is identical to SMOTEBagging [79].
\nMost classification algorithms ignore various misclassification errors and consider that all these errors have the same cost. In many real-world applications, this hypothesis is not true because the difference between different classification errors can be quite large.
\nCost-sensitive learning methods [80, 81] have been given a lot of attention in recent years to address this problem. They have been divided into two categories: direct cost-sensitive learning [80] and cost-sensitive meta learning [80, 82]. They are also used for imbalanced datasets such as boosting and SVMs.
\nIn each iteration of boosting, the weights of misclassified instances increase by the same ratio whatever their classes. However, in imbalanced datasets, the number of misclassified minority instances is higher. Hence, it is necessary to distinguish between different sorts of instances in the weight attribution phase. Therefore, the higher weights may be attributed to the minority instances in order to be well classified. To achieve this goal, misclassification costs are introduced into the weight update equation. Among these algorithms [83, 84], we have AdaC1, AdaC2, and AdaC3. They differ in the way of introducing the misclassification costs into the weight update formula within the exponential part and into the equation of the calculation of classifier performance.
\nSVMs [63] have been integrated with sampling methods to deal with the classification problem in imbalanced datasets. Among these methods, we have the following.
\nSDC results from the application of SMOTE with different error costs (DEC) [32]. This method aims to shift the decision boundary far from minority instances and to increase their number. To achieve the first objective, Veropoulos et al. [85] proposed the use of different costs for the minority and majority classes. The minority instances are also duplicated by SMOTE to make them densely distributed in order to guarantee the most well-defined boundary.
\nThese methods [86] balance the training dataset by preprocessing and providing it to SVM for building an optimal classifier. For instance, the ensemble of undersampling SVMs (EUS-SVM) applies SVM, N times on N different training datasets. It contains all minority instances and some majority instances selected by random sampling. The final classifier is built by the combination of N produced classifiers.
\nThis approach [87] applies an intelligent method to the extraction of the classification rules from imbalanced binary datasets based on three phases.
\nIn phase 1, a learning algorithm is developed based on a genetic algorithm with the aim of extracting the first classifier noted C1, which covers only majority instances when available. Majority instances, which are well classified by the rules of C1, are removed. This approach balances the imbalanced dataset and prevents the loss of information contained in the deleted majority instances, which are replaced by the classification rules of C1. The number of deleted majority instances depends on the value of IR; the process is carried out until the IR is equal to 1. The genetic algorithm is used to find the “best” rule for the majority class from the imbalanced dataset. The quality of each rule is evaluated by satisfying specificity (defined in Eq. (4)). A rule is the best if it has a high number of well-classified majority instances.
\nIn phase 2, the same procedure is applied to the obtained balanced dataset using a fivefold cross-validation to construct the classifier C2, which contains rules that represent both majority and minority instances. In this phase, the quality of each rule is evaluated by maximizing the accuracy (Eq. (14)).
\nIn the third phase, they merge C1 and C2 to produce the classifier C3 at first, and then they process the obtained classifier C3 by eliminating the specific and contradictory rules.
\nACOSampling [13] is a method of undersampling based on ant colony optimization [88]. It handles imbalanced DNA microarray datasets. It consists of extracting the balanced dataset S′ for the original dataset S as follows:
For T times, it divides the dataset S into two datasets, training and validation.
Each training dataset Si is processed by applying modified ACO algorithm [88] to filter less informative majority instances and search the corresponding optimal training dataset Si′.
The statistical results from the T optimal training datasets are given in the form of a list of frequencies, where each frequency indicates the importance of the corresponding majority instance. The extracted instances are those with high frequency, which will be combined with all minority instances to construct the final balanced training dataset S′.
It produces one classifier by support vector machines using S′.
In this chapter, we have presented the classification problem in imbalanced datasets, which are composed of two kinds of instances: majority instances and minority ones. We have also presented the different approaches and techniques used to handle this problem which are divided in three levels: data level, algorithm level, and hybrid level.
\nIn future work, we are planning to present a state of the art about different approaches and techniques used to handle the classification problem in multi-class imbalanced datasets. Also, we will extend our proposed approach to this kind of datasets.
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DC.",slug:"advance-in-phytoremediation-research",totalDownloads:7491,totalCrossrefCites:2,totalDimensionsCites:8,abstract:null,book:{id:"5331",slug:"advanced-knowledge-application-in-practice",title:"Advanced Knowledge Application in Practice",fullTitle:"Advanced Knowledge Application in Practice"},signatures:"Woranan Nakbanpote, Natthanwoot Paitlertumpai, Kannika Sukadeetad, Orapan Meesungeon and Wattchara Noisa-nguan",authors:null}],mostDownloadedChaptersLast30Days:[{id:"69447",title:"Importance of Social Networks for Knowledge Sharing and the Impact of Collaboration on Network Innovation in Online Communities",slug:"importance-of-social-networks-for-knowledge-sharing-and-the-impact-of-collaboration-on-network-innov",totalDownloads:822,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Innovation results from interactions between different sources of knowledge, where these sources aggregate into groups interacting within (intra) and between (inter) groups. Interaction among groups for innovation generation is defined as the process by which an innovation is communicated through certain channels over time among members of a social system. Apart from the discussion about knowledge management within organizations and the discussion about social network analysis of organizations on the topic of innovation and talks about various trade-offs between strength of ties and bridging ties between different organizational groups, within the topic of open source software (OSS) development researchers have used social network theories to investigate OSS phenomenon including communication among developers. It is already known that OSS groups are more networked than the most organizational communities; In OSS network, programmers can join, participate and leave a project at any time, and in fact developers can collaborate not only within the same project but also among different projects or teams. One distinguished feature of the open source software (OSS) development model is the cooperation and collaboration among the members, which will cause various social networks to emerge. In this chapter, the existing gap in the literature with regard to the analysis of cluster or group structure as an input and cluster or group innovation as an output will be addressed, where the focus is on “impact of network cluster structure on cluster innovation and growth” by Behfar et al., that is, how intra- and inter-cluster coupling, structural holes and tie strength impact cluster innovation and growth, and “knowledge management in OSS communities: relationship between dense and sparse network structures.” by Behfar et al., that is, knowledge transfer in dense network (inside groups) impacts on knowledge transfer in sparse network (between groups).",book:{id:"8850",slug:"harnessing-knowledge-innovation-and-competence-in-engineering-of-mission-critical-systems",title:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems",fullTitle:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems"},signatures:"Stefan K. Behfar",authors:[{id:"293475",title:"Dr.",name:"Stefan Kambiz",middleName:null,surname:"Behfar",slug:"stefan-kambiz-behfar",fullName:"Stefan Kambiz Behfar"}]},{id:"69883",title:"Evolution of Communication Skills in Virtual Product Development Process: Experience From EGPR",slug:"evolution-of-communication-skills-in-virtual-product-development-process-experience-from-egpr",totalDownloads:749,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"More than a decade of continuous international collaboration of several European universities in teaching new product development in virtual environment gives unique opportunity to investigate evolution and development of communication techniques for NPD collaboration in virtual environment. This chapter provides theoretical and practical view on different aspects: technical evolution of ICT tools, development and fostering of communication flow, personal aspects of IT communication, with important emphasis on building of trust within virtual teams. The reader can extract from this chapter guidelines for work in collaborative virtual environment, to run effectively either small projects, meetings and lectures or even more complex projects, distributed among several dislocated teams. The chronological overview of the continuous virtual communication in the last 15 years gives also fair suggestions about future evolution for the next decade.",book:{id:"8850",slug:"harnessing-knowledge-innovation-and-competence-in-engineering-of-mission-critical-systems",title:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems",fullTitle:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems"},signatures:"Nikola Vukašinović, Janez Benedičič and Roman Žavbi",authors:[{id:"294317",title:"Dr.",name:"Nikola",middleName:null,surname:"Vukašinović",slug:"nikola-vukasinovic",fullName:"Nikola Vukašinović"},{id:"294322",title:"Prof.",name:"Roman",middleName:null,surname:"Žavbi",slug:"roman-zavbi",fullName:"Roman Žavbi"},{id:"308791",title:"Dr.",name:"Janez",middleName:null,surname:"Benedičič",slug:"janez-benedicic",fullName:"Janez Benedičič"}]},{id:"70099",title:"Knowledge Redundancy Cycles in Complex Mission-Critical Systems",slug:"knowledge-redundancy-cycles-in-complex-mission-critical-systems",totalDownloads:714,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Based on a 20-year, 10-million case study programme of research, 98% of all innovation attempts end in failure. The main aim of the research has been to decode the underpinning, first-principle-driven ‘DNA’ of the 2% of successful attempts. Sitting right at the centre of this DNA is a triad of fundamentals: the need to embrace the dynamics of complex adaptive systems, the need to actively seek out and eliminate compromises and contradictions, and the need for industry domains to periodically unlearn knowledge that has become redundant. The chapter discusses all three of these pillars. Particular attention is paid to the knowledge redundancy topic, where the fact that the life-cycle of knowledge follows distinct, repeating patterns of evolution at meta, macro and micro- hierarchical levels is demonstrated. The research further demonstrates how organizations can use these patterns to objectively identify redundancy ‘pulse-rates’ and thus objectively manage both the acquisition of required new knowledge and the disposal of knowledge that is no longer fit for purpose. The research shows too that a key aspect of this ‘unlearning’ activity demands that organizational leaders acknowledge and accommodate the very human emotions that accompany change initiatives where the things that define a person’s competence become a hazard to the future success of the enterprise.",book:{id:"8850",slug:"harnessing-knowledge-innovation-and-competence-in-engineering-of-mission-critical-systems",title:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems",fullTitle:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems"},signatures:"Darrell Mann",authors:[{id:"297423",title:"Prof.",name:"Darrell",middleName:null,surname:"Mann",slug:"darrell-mann",fullName:"Darrell Mann"}]},{id:"69932",title:"Simplexity: A Hybrid Framework for Managing System Complexity",slug:"simplexity-a-hybrid-framework-for-managing-system-complexity",totalDownloads:726,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Knowledge management, management of mission critical systems, and complexity management rely on a triangular support connection. Knowledge management provides ways of creating, corroborating, collecting, combining, storing, transferring, and sharing the know-why and know-how for reactively and proactively handling the challenges of mission critical systems. Complexity management, operating on “complexity” as an umbrella term for size, mass, diversity, ambiguity, fuzziness, randomness, risk, change, chaos, instability, and disruption, delivers support to both knowledge and systems management: on the one hand, support for dealing with the complexity of managing knowledge, i.e., furnishing criteria for a common and operationalized terminology, for dealing with mediating and moderating concepts, paradoxes, and controversial validity, and, on the other hand, support for systems managers coping with risks, lack of transparence, ambiguity, fuzziness, pooled and reciprocal interdependencies (e.g., for attaining interoperability), instability (e.g., downtime, oscillations, disruption), and even disasters and catastrophes. This support results from the evident intersection of complexity management and systems management, e.g., in the shape of complex adaptive systems, deploying slack, establishing security standards, and utilizing hybrid concepts (e.g., hybrid clouds, hybrid procedures for project management). The complexity-focused manager of mission critical systems should deploy an ambidextrous strategy of both reducing complexity, e.g., in terms of avoiding risks, and of establishing a potential to handle complexity, i.e., investing in high availability, business continuity, slack, optimal coupling, characteristics of high reliability organizations, and agile systems. This complexity-focused hybrid approach is labeled “simplexity.” It constitutes a blend of complexity reduction and complexity augmentation, relying on the generic logic of hybrids: the strengths of complexity reduction are capable of compensating the weaknesses of complexity augmentation and vice versa. The deficiencies of prevalent simplexity models signal that this blended approach requires a sophisticated architecture. In order to provide a sound base for coping with the meta-complexity of both complexity and its management, this architecture comprises interconnected components, domains, and dimensions as building blocks of simplexity as well as paradigms, patterns, and parameters for managing simplexity. The need for a balanced paradigm for complexity management, capable of overcoming not only the prevalent bias of complexity reduction but also weaknesses of prevalent concepts of simplexity, serves as the starting point of the argumentation in this chapter. To provide a practical guideline to meet this demand, an innovative model of simplexity is conceived. This model creates awareness for differentiating components, dimensions, and domains of complexity management as well as for various species of interconnectedness, such as the aligned upsizing and downsizing of capacities, the relevance of diversity management (e.g., in terms of deviations and errors), and the scope of risk management instruments. 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He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. 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He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. 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She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. 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Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. Voyich",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Montana State University",country:{name:"United States of America"}}},{id:"330412",title:"Dr.",name:"Muhammad",middleName:null,surname:"Farhab",slug:"muhammad-farhab",fullName:"Muhammad Farhab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"349495",title:"Dr.",name:"Muhammad",middleName:null,surname:"Ijaz",slug:"muhammad-ijaz",fullName:"Muhammad Ijaz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}}]}},subseries:{item:{id:"20",type:"subseries",title:"Animal Nutrition",keywords:"Sustainable Animal Diets, Carbon Footprint, Meta Analyses",scope:"An essential part of animal production is nutrition. Animals need to receive a properly balanced diet. One of the new challenges we are now faced with is sustainable animal diets (STAND) that involve the 3 P’s (People, Planet, and Profitability). We must develop animal feed that does not compete with human food, use antibiotics, and explore new growth promoters options, such as plant extracts or compounds that promote feed efficiency (e.g., monensin, oils, enzymes, probiotics). These new feed options must also be environmentally friendly, reducing the Carbon footprint, CH4, N, and P emissions to the environment, with an adequate formulation of nutrients.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11416,editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. He teaches various degree courses in zootechnics, sheep production, and agricultural sciences and natural resources.\n\nDr. Ronquillo’s research focuses on the evaluation of sustainable animal diets (StAnD), using native resources of the region, decreasing carbon footprint, and applying meta-analysis and mathematical models for a better understanding of animal production.",institutionString:null,institution:{name:"Universidad Autónoma del Estado de México",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,series:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517"},editorialBoard:[{id:"175762",title:"Dr.",name:"Alfredo J.",middleName:null,surname:"Escribano",slug:"alfredo-j.-escribano",fullName:"Alfredo J. 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