Emblematic research in behavioral pharmacology.
\\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!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 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 252 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!
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Earthquakes can range in size from those that are so weak that they cannot be felt to those violent enough to toss people around and destroy the whole cities. At the Earth's surface, earthquakes manifest themselves by shaking and sometimes displacement of the ground. When the epicenter of a large earthquake is located offshore, the seabed may be displaced sufficiently to cause a tsunami. Earthquakes can also trigger landslides and occasionally volcanic activity. Earthquakes are caused not only by rupture of geological faults but also by other events such as volcanic activity, landslides, mine blasts, and nuclear tests. This book addresses the multidisciplinary topic of earthquake hazards and risk, one of the fastest growing, relevant, and applied fields of research and study practiced within the geosciences and environment. This book addresses principles, concepts, and paradigms of earthquakes, as well as operational terms, materials, tools, techniques, and methods including processes, procedures, and implications.",isbn:"978-1-78923-950-8",printIsbn:"978-1-78923-949-2",pdfIsbn:"978-1-83881-557-8",doi:"10.5772/intechopen.71298",price:139,priceEur:155,priceUsd:179,slug:"earthquakes-forecast-prognosis-and-earthquake-resistant-construction",numberOfPages:334,isOpenForSubmission:!1,hash:"dfe07735f73c9267f1d69a5c916c7135",bookSignature:"Valentina Svalova",publishedDate:"October 31st 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6564.jpg",keywords:null,numberOfDownloads:9341,numberOfWosCitations:2,numberOfCrossrefCitations:9,numberOfDimensionsCitations:11,numberOfTotalCitations:22,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 20th 2017",dateEndSecondStepPublish:"December 11th 2017",dateEndThirdStepPublish:"February 9th 2018",dateEndFourthStepPublish:"April 30th 2018",dateEndFifthStepPublish:"June 29th 2018",remainingDaysToSecondStep:"3 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"62677",title:"Dr.",name:"Valentina",middleName:null,surname:"Svalova",slug:"valentina-svalova",fullName:"Valentina Svalova",profilePictureURL:"https://mts.intechopen.com/storage/users/62677/images/system/62677.jpeg",biography:"Graduated from Moscow State University, Mechanical-Mathematical Faculty.\r\nPh.D. (Physical-Mathematical Sciences), 1975. Thesis 'Mechanical-Mathematical Modelling of the Lithosphere Geodynamics”.\r\n Leading Scientist, Head of International Projects Department of Sergeev Institute of Environmental Geoscience, Russian Academy of Sciences. \r\n\r\nScientific research fields:\r\nMechanical-mathematical modelling in geology, geothermal investigations, computer modelling, geothermal energy use, paleoclimate changes and reconstruction, sustainable development, environmental problems decision, natural hazards, landslides, risk analysis.\r\nMore than 400 scientific publications.\r\nResults of research were presented at more than 100 International Scientific Conferences and Congresses in more than 50 countries.\r\n\r\nMember of International and Scientific Organizations:\r\n International Geothermal Association (IGA).\r\nIGA Board of Directors . \r\nPresident of Geothermal Energy Society (GES) of Russia.\r\nAssociate Member of the International Informatization Academy.\r\nIAMG (International Association for Mathematical Geosciences).\r\nScientific Secretary of Geothermal Council of Russia, Russian Academy of Sciences. \r\nICL (International Consortium on Landslides). \r\nICL BoR (International Consortium on Landslides, Board of Representatives).\r\nInternational Best Paper Award 'PRESSZVANIE”, nomination 'Clean Energy”, 2015.",institutionString:"Sergeev Institute of Environment Geoscience, Russian Academy of Sciences",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Sergeev Institute of Environmental Geoscience",institutionURL:null,country:{name:"Russia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"778",title:"Earthquake Engineering",slug:"engineering-environmental-engineering-earthquake-engineering"}],chapters:[{id:"62753",title:"Introductory Chapter: Earthquakes, Life at 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Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"73245",title:"New Developments in Behavioral Pharmacology",doi:"10.5772/intechopen.93700",slug:"new-developments-in-behavioral-pharmacology",body:'\nEvery time academics talk about the evolution of human societies and the advance of humanity, language is always mentioned, followed by different pieces of technology that allowed us to change the world. Few times, medicine is mentioned, and within the same area of knowledge, pharmacology is even more frequently omitted. But without the development of pharmacology as a science founded in systematic research, the capacities of medical sciences and therapeutics would be very limited. Knowledge in pharmacology allows us to understand that there exist chemical substances with very specific structures and properties which, in controlled doses, can interact with the normal physiology of our organism in order to produce effects that improve our health, known as therapeutic effects; but if the doses are insufficient or excessive, the effects will be useless or harmful (toxic), respectively [1]. These substances responsible for the actions of medicines are named as active compounds.
\nMost of the active compounds used in medicine were consumed together with the organism which contained them, most frequently plants. As chemistry advanced, scientists succeed in isolating these compounds and described their chemical structure. In consequence, laboratories started to synthesize these substances and others with a similar structure that should be tested in research laboratories before using them to treat diseases in humans [2].
\nNowadays, pharmacological research has grown beyond treatments for infectious agents, covering diseases related to the alteration of the normal functioning of the central nervous system (CNS). There are medications to treat disorders such as depression, anxiety, chronic pain, attention deficit and hyperactivity disorder, epilepsy, and Parkinson’s disease, and new drugs are desperately sought to stop Alzheimer’s disease. On the other hand, one of the most important current health problems is related to the addictive behaviors triggered by the consumption of certain substances and the side effects of these addictions: respiratory and cardiovascular diseases in the case of tobacco, metabolic diseases in the case of alcoholism and addictive consumption of refined sugars, infectious diseases in the case of injected drugs, and many others that are not mentioned here. Without losing sight of the fact that addiction is itself a disease of the nervous system with devastating effects per se on the patient’s quality of life. In several countries, prescription of different therapeutic agents acting on the CNS to treat psychiatric disorders, such as antidepressants, antipsychotics, and stimulants, has increased [3, 4] as in the case of methylphenidate and amphetamines in different countries such as United States [5] and the Netherlands [6]. The same way, antidepressant users have increased markedly around the world in countries such as Norway, Sweden, and Denmark [7], among others. Additionally, the use of different substances of abuse such as tobacco [8] and marijuana has increased in the population [9]. Also, the development of new technologies and products has a significant impact on mental health as the discovery of Internet addiction [10] and the addictive consumption of refining sugar [11, 12], which impacts on the behavior of subjects. All these make important the continuous development of behavioral pharmacology in order to cope with the challenges in mental health.
\nBehavioral pharmacology, also known as psychopharmacology, has developed as an interdisciplinary science that comprises fields such as neuroethology, neurochemistry, pharmacology and neuropharmacology, psychophysiology, neurophysiology, experimental analysis of behavior, and several other fields related to neurosciences [13]. Behavioral pharmacology is founded on systematic research with precise methods for assessing and interpreting the effects of chemical, hormones, and drugs on the behavior in humans and experimental animals in order to establish its potential as therapeutic agents or pharmacologic tools to explore how the brain functions and the underlying neurobiological mechanism of cognition, emotions, and behavior. Behavioral pharmacology must thus be an integral component of many neuroscience research programs [14].
\nIn this sense, the development of behavioral pharmacology comprises the development of areas as pharmacology and psychology, experimental analysis of behavior, and recently neuroscience. For a historical review, see [14, 15, 16]. However, research in behavioral pharmacology can be summarized in: (1) the development of procedures to screen pharmacological agents for potential clinical effectiveness. (2) Perfecting behavioral techniques to explore the mechanisms of action of behaviorally active drugs and using these chemicals and drugs as tools for the analysis of complex behaviors (i.e., when drugs reinforce behavior and when drugs serve as discriminative stimuli) [16] (see Table 1). Therefore, drugs are not only a subject of study, because of its behavioral effects but are also a piece of technology that helps to elucidate how behaviors are controlled by living organisms.
\nYear | \nDescription | \nReference | \n
---|---|---|
1936 | \nSelye H. described the impact of several types of adverse stimuli on animal health, in the form of a syndrome characterized by three phases: alarm, adaptation, and exhaustion, which can lead to death if stimuli are maintained. This syndrome was later named as the stress response which has been intensively studied and strongly associated with the impairment of brain function in animals or the development of mental disorders in humans | \n[17] | \n
1972 | \nThe first study to administrate Delta-9-tetrahydrocannabinol in humans to test the effects on sleep patterns is carried out. The results show a decrease in sleep onset latency. To date, there are controversial results about the positive effects the cannabis on sleep quality | \n[18] | \n
1977 | \nThe forced swim test is proposed as a behavioral tool to explore the effects of antidepressant drugs in rats and mice that are exposed to a stressful inescapable condition that triggers despair behavior (immobility) | \n[19] | \n
1986 | \nElevated plus maze is developed as a tool to measure anxiety-like behaviors of the rat and test substances with potential anxiolytic effects | \n[20] | \n
1988 | \nModafinil was prescribed for the first time for the treatment of narcolepsy and idiopathic hypersomnia in patients | \n[21] | \n
2005 | \nThis study explored the behavioral and neuronal response to stress in ovariectomized rats (OVX). These rats were more sensitive to stress, which was associated with a low concentration of steroid hormones. This effect was prevented by restitution with 17-β estradiol | \n[22] | \n
2006 | \nAnxiety-like behavior is dependent on the post-ovariectomy time frame. At 12-week post-ovariectomy there is more anxiety-like behavior than a 3-week post-ovariectomy | \n[23] | \n
2016 | \nThe first systemic review and meta-analysis that discuss the effects of the orexin agonist Suvorexant for the treatment of insomnia. Suvorexant improved some sleep parameters, but some adverse effects were reported | \n[24] | \n
2019 | \nIn this study, it was identified that at 3-week post-ovariectomy appears anxiety-like behavior, but from 6-week post-ovariectomy in addition to anxiety-like behavior, also increases depression-like behavior in rats, supporting an experimental model of surgical post-menopause | \n[25] | \n
Emblematic research in behavioral pharmacology.
Behavior is a biological property of organisms, which remarks on the significance of the study of drug-behavior interactions [15]. Maybe, a great example of the impact of behavior beyond psychology is the research by ethologists K. Lorenz, N. Tinbergen, and K. von Frisch, which focused on the analysis of behavior in several species including fish, insects, and birds, and the importance of which made them worthy of the Nobel price of medicine in 1973 “for their discoveries concerning organization and elicitation of individual and social behaviour patterns.”
\nThe first step in all behavioral sciences has been to define what is behavior; it could seem an easy task, but historically many different definitions of behavior have been used by scientists over the time, and even the knowing of a unique definition is elusive and may be useless for every different area such as psychology, ethology, and experimental analysis of behavior, among others; for review see [26, 27]. As mentioned before, one of the directions of behavioral pharmacology was the development of procedures to screen the effects of pharmacological agents on specific behaviors under controlled environments. This approach allows scientists to work with operational definitions of specific behaviors, for example, exploration can be measured by scoring ambulation, rearing or nose approaching to an object; sexual behavior can be measured by conditioned place preference, number of mounts, latency and number of ejaculations. All these behaviors are normally studied under controlled environments that are designed specifically to the required behavioral display and every feature of the environment; the experimental subjects or chemical agents with probed effects on humans have been studied in this environment with the purpose of establishing these manipulations as models of a specific behavior (see Table 2) as spatial learning and memory, or models of specific pathologies behaviorally expressed as is the case of anxiety [28], depression [29], obsessive compulsive disorder [30], Parkinson [31], epilepsy [32] or addictive behaviors [33], and sleep deprivation [34], among others.
\nResearch area | \nDescription | \n
---|---|
Hormone restitution therapy | \nThis review discussed, 25 years ago, the importance of steroid hormones in the regulation of behavior and some psychiatry disorders; particularly depression associated with premenstrual syndrome and the transition to menopause. Also, it discusses some research about the role of hormone restitution therapy in ameliorating depression symptoms [35] | \n
Sexual dimorphism | \nThis review discusses preclinical and clinical research that show how hormones are involved in the sex differences in some psychiatric disorders like anxiety, and their interactions between fear, stress, and gonadal hormones [36] | \n
Behavioral animal models | \nThis research reviews the relevance of non-mammalian models in behavioral pharmacology with application in the development of biological psychiatry [37] | \n
Behavioral model of menopause | \nThis review highlights the importance of animal models of menopause in the understanding of neurobiological changes associated with the long-term absence of ovarian hormones. To then elucidate novel perspectives and interventions to improve the life quality in the menopausal women under a translational context [38] | \n
Sleep and insomnia | \nThis review describes the efficacy of new drugs in the treatment of insomnia such as melatonin, Remelteon, Tasimelteon, and Suvorexant, among others [39] | \n
Hormones and behavior | \nThis review discusses the influence of hormones on brain function and behavior, and integrate information to explain how the brain and the body communicate reciprocally via hormones and other mediators, and in ways that influence brain and body health but which can also accelerate diseases processes when the mediators of allostasis are dysregulated [40] | \n
Addiction | \nA review of the most popular behavioral models for the study of addictions such as conditioned place preference and self-administration and new models to study behavioral addictions as gambling and exercise addiction [33] | \n
Sleep disorders | \nThis review describes the Pitolisant (Wakix®), first-in-class antagonist/inverse agonist of the H3 receptor for the treatment of narcolepsy with or without cataplexy [41] | \n
Current topics in behavioral pharmacology.
Animals are used as proxies for human phenomena throughout the literature, and the exact definition of what constitutes a “model” can be confusing. In behavioral pharmacology, a field that intersects between psychology, neuroscience, and pharmacology [42], different uses are attributed to different epistemic operations and, as a consequence, to different definitions of validity [43, 44]. One of the most basic definitions is that by Paul Willner, which defined screening tests as those uses of animal behavior that are capable of discriminating between different drug effects (i.e., possess high predictive validity); behavioral bioassays as those uses of animal behavior that are capable of shedding light on the neural basis of normal behavior (i.e., possess high face validity); and simulations as those uses of animal behavior that can inform on the etiology, pathophysiology, and treatment of human (mental) disorders (i.e., possess high construct validity). Further developments of this framework [45] advance the theory of validity, therefore improving the capability of researchers to evaluate animal models.
\nScreening tests show good predictive validity in that they are able to detect the effects of drugs, which are already known to have clinical efficacy; as a result, they are likely to be able to predict the effect of new drugs, which show similar biochemical or behavioral effects in the test [42, 43]. Examples include most uses of the tail suspension test and forced swim tests, which are commonly referred to as models of depression but actually do not simulate the etiological and pathophysiological aspects of human depression. When used without any further manipulations of the animal (i.e., lesions, genetic manipulations, or other stressors which are thought to be causally related to depression), these tests are good at discriminating drugs which act as serotonin reuptake inhibitors and reasonably good at predicting antidepressant efficacy. Since screening tests rely mostly on predictive validity, current approaches to modeling in behavioral pharmacology view them as limited. Moreover, producing models which show good construct validity in at least some domains (i.e., epidemiology, symptomatology and natural history, genetics, biochemistry, etiology, histological alterations, or endpoints) has been proposed as a way to indirectly increase predictive validity [46], as drugs which improve performance in a test that simulates at least some aspects of the target disorder.
\nBehavioral bioassays are tests that use nonhuman animals to try to understand the histological, electrophysiological, biochemical, and genetic bases of neurobehavioral functions [42, 43]. Usually, bioassays are used to understand normal functioning, instead of pathological alterations in these psychological processes. They rely on face validity—that is, how much performance in the test “resembles” the target human function. Of course, taken “as is,” face validity runs a great risk of anthropomorphism, and the resemblance should not be sought at the topography level, but at the functional level [47]. For example, the elevated plus-maze, when used as a test per se (and not as an endpoint in a simulation), is interpreted as a behavioral bioassay of anxiety due to the functional role of thigmotaxis in rodent defensive behavior [48, 49]. Of course, this comparison only makes sense if we consider that anxiety is a normal mechanism that is associated with defensive behavior [50, 51]. Thus, the face validity of a test is only as good as our psychological/behavioral theory about a given function (i.e., anxiety, fear, memory, and attention, among others) [47].
\nFinally, simulations are tests, which use nonhuman animals to try to understand a human disorder from the point of view of etiology and pathophysiology [42, 43]. Most approaches to psychopathology currently frame disorders in a diathesis-stress theory [45], which assumes that vulnerabilities (general or specific; genetic, developmental, or temperamental) increase the probability of developing a specific disorder when the individual passes through general or specific stressors. In analogy, to develop a simulation of a mental disorder in a nonhuman animal, the vulnerabilities and stressors should be modeled, transforming an “initial organism” into a “vulnerable organism” and this latter into a “pathological organism,” in which behavioral endpoints are assessed and biomarkers evaluated [44, 45]. From all senses of “behavioral model,” the simulation is the one that better approaches the idea of modeling a disease [42, 44], but is also the more time-consuming. Moreover, to increase the construct validity of a simulation, aspects such as etiology and pathophysiology should be taken into consideration, but sometimes these aspects are unknown and are precisely what is under investigation [42]. Thus, high construct validity needs to be balanced against practical constraints, and therefore no behavioral simulations with optimal characteristics exist [52]. In the next pages some examples of these “behavioral models” are described in order to introduce the present book.
\nUnder the framework discussed above for behavioral models, interesting approaches have appeared using non-rodent species. While mice and rats are still the most widely used model organisms in behavioral pharmacology [53], zebra fish (Danio rerio Hamilton 1822) come in an honorable third place, quickly “swimming into view” as a relevant model organism in this field [54]. The “classical” criteria for selecting a model organism in genetics and developmental biology—small size, fast (and external) development, easy reproduction, low cost, genetic tractability [55]—are present in zebra fish [37]. Moreover, other advantages are also described by zebra fish researchers: phylogenetic position; intermediate complexity in physiology and throughput; availability of tools to study neurocircuitry and to interfere in normal function (i.e., expression vectors, pharmacogenomic tools, and advanced microscopy); a productive community of researchers; and accumulation of significant data and methodological developments [37]. The combination of these characteristics suggested that zebra fish could be a suitable model organism in behavioral pharmacology.
\nCurrently, very few true simulations exist in zebra fish, and most behavioral tests that are used to study psychiatric disorders in this species are actually screening tests or behavioral bioassays. This is a consequence of an extensive focus of the research in the field in the last 20 years on developing behavioral tests. This step, of course, was necessary to galvanize research in the field. Notable exceptions exist, but—as is the case with most initial work on using model organisms to study disorders and investigational treatments—these are still limited. However, past research has identified and allowed to control factors that affect zebra fish behavioral tests. Now it is clear how chemical properties of the water, illumination, number of fish per tank and routes of administration modify pharmacological effects. For example, administration by immersion is useful for chronic treatments but lacks a precise control of the doses absorbed [56], on the other hand, intraperitoneal administrations ensure the absolute control of doses but are not useful for chronic treatments due to the stress that produce [57]. Oral administration through drugs incorporated in the food is useful for chronic treatments and controlling the doses is easier than immersion [58], however chemical properties of the drug determine their ability to hold into the food until swallowed and oral metabolism must be considered. With the standardization of the proper protocols these factors can be controlled, and its effects limited so, behavioral pharmacology research with zebra fish is still a suitable and growing field.
\nThe zebra fish light/dark test [59] and the novel tank test [60] are widely used to test the effects of different drugs on anxiety-like behavior in this species. These tests rely on natural preferences observed in the wild, and display excellent remission validity—that is, they are sensitive to drugs which affect anxiety in clinical settings, and not sensitive to drugs which do not affect anxiety [61]. As a result, these tests were used as screening tests to investigate new drugs, including drugs derived from natural products and plants, for example, refs. [62, 63]. These tests have also been used to study the neural mechanisms of anxiety-like behavior [64, 65, 66, 67, 68]. Thus, these tests can be used both as screening tests and as behavioral bioassays.
\nThe behavior of adult zebra fish is more complex than the behavior of larvae, but its throughput is smaller. Throughput can be increased by testing larval behavior in microplates [69]. Light levels and stimuli can be delivered simultaneously to many larvae at once, increasing throughput and reproducibility. For example, the photo-motor response (a stereotypic series of motor behaviors that are elicited by high-intensity light) is sensitive to a wide range of psychoactive drugs and able to predict mechanisms of action of drugs, which were previously not investigated in rodents [70]. A battery of assays has been proposed in larval zebra fish that is highly sensitive to antipsychotics and able to identify haloperidol-like compounds [71]. While suffering from the low face and construct validity these assays show very good predictive validity, and therefore are suitable as screening tests.
\nExamples of simulations can be found in the field of neurological disorders [72]. An interesting example is the generation of mutants with differences in genes known to be associated with diseases. In humans, mutations in the SCN1A gene, which encodes a voltage-gated sodium channel, causes Dravet syndrome, characterized by severe intellectual disability, impaired social development, and drug-resistant seizures. The scn1Lab mutant zebra fish displays spontaneous seizure-like electroencephalogram activity, convulsive-like motor patterns, and hyperactivity [73]. These mutants have been used to investigate drugs, which could be used to treat Dravet syndrome in human patients; drugs that affect the serotonergic system have been found to ameliorate the symptoms in the mutants [74], and suggest interesting avenues for human patients.
\nNow, we will review the role of behavioral pharmacology on a subject extensively explored in human trials: sleep.
\nPharmacological treatment of sleep disorders is still partially known and not well understood. Currently, extensively pharmacological research is focused in two sleep disorders: insomnia and narcolepsy. Insomnia is defined as the individual’s inability to fall asleep, manifested by a long latency to sleep onset and frequent nighttime awakenings experienced three times per week or more, for at least 1 month [75]. Insomnia causes emotional disturbances, impairs cognition, and reduced quality of life [76, 77]. Most epidemiologic studies have found that about one-third of adults (30–36%) report at least one symptom of insomnia, like difficulty initiating sleep or maintaining sleep [78]. Currently, benzodiazepines or Z-drugs (zopiclone, zolpidem, or zaleplon) are the first options to treat insomnia. These drugs act as positive allosteric modulators at the GABAA binding site, potentiating GABAergic inhibitory effects [79]. However, short-term or long-term treatment with these drugs has undesirable effects such as cognitive or memory impairment, the rapid development of tolerance, rebound insomnia upon discontinuation, car accidents or falls, and a substantial risk of abuse and dependence [39, 80, 81], which make necessary research on new potential therapeutic agents.
\nAccording to the new evidence-based clinical practice guidelines for the treatment of insomnia [75], new pharmacology agents for insomnia management are implemented (Table 3).
\nDrugs | \nSite of action | \nTherapeutic effect | \n
---|---|---|
Antidepressant (trazodone, mirtazapine, olanzapine, and quetiapine) | \nAgonists of the serotonin receptor 5-HT2A and 5-HT2C\n | \nModerate improvement in subjective sleep Little improvement in sleep efficiency [82] | \n
Antiparkinsonian ropinirole | \nAgonist of the dopamine receptor D2 | \nImprovement in efficiency of sleep and total time slept [83] | \n
Suvorexant | \nAntagonist of the orexin receptor | \nImprovement of sleep onset and subjective total slept time compared to placebo [84] | \n
Ramelteon | \nDual agonist of both MT1 and MT2 melatonin receptors | \nImprovement in latency to persistent sleep, total sleep time and sleep efficiency [85] | \n
Diphenhydramine | \nAgonist of the histaminergic receptors | \nNo clear beneficial impact on sleep [86] | \n
New drugs used to insomnia management.
On the other hand, Type 1 narcolepsy (narcolepsy with hypocretin deficiency) is a chronic neurodegenerative sleep disorder caused by a deficiency of hypocretin-producing neurons in the lateral hypothalamus (LH). Hypocretin neurons are involved in the control of the sleep-wake cycle [87]. Treatment of narcolepsy is traditionally based on amphetamine-like stimulants that enhance dopaminergic release to improve narcoleptic symptoms. Nonetheless, a new group of drugs is arising as a forthcoming treatment of narcolepsy.
\nPitolisant (Wakix®) is an inverse agonist of the histamine H3 auto-receptor that not only blocks the braking effect of histamine or H3 receptor agonists on endogenous histamine release from depolarized synaptosomes but also enhances histamine release over the basal level (even at low nanomolar concentrations) in the structures as hypothalamus and cerebral cortex [88]. The administration of 20 mg/kg of Pitolisant promoted wakefulness, and decreased abnormal direct REM sleep onset in narcoleptic hypocretin knockout mice by enhancing histaminergic and noradrenergic activity [89]. Pitolisant seem a safe therapeutic option since doses of 120 mg once a day in the morning, that represent six times the therapeutic, doses did not produce adverse effects and plasma levels reduced at the end of the day, ensuring a lack of waking effect during the night [90]. Additionally, adverse effects due to metabolic drug-drug interaction are low since Pitolisant is metabolized by two distinct CYP450 isoforms. For example, the administration of 40 mg of Pitolisant together with 10 mg of Olanzapine to a group of healthy volunteers did not change drug plasma levels compared to only one drug administration [91].
\nAny chapter on behavioral pharmacology would be incomplete without a section reviewing the effects of certain hormones. Behavioral, emotional and affective states are influenced by plasma and brain concentration of steroid hormones in diverse organisms. Particularly, in nonhuman primates and humans there is significant sexual dimorphism respect to behavior and emotional states. Initially, the attributed properties of steroid hormones were related to the maintaining of secondary sexual characters and reproductive function, but some decades ago, it has been established that steroid hormones also influence behavior and some psychiatric disorders. Expression of anxiety- and depression-related behaviors depends on plasma and brain levels of steroid hormones; which in vulnerable subjects could predispose to development of some psychiatric disorder [92].
\nIn humans, anxiety and depression symptoms are more frequent in women than men in a proportion of 3:1. These differences have been attributed to differences in the concentration of steroid hormones. Particularly in women, a high incidence of anxiety and depression symptoms has been identified during physiological states characterized by low concentration of steroid hormones (i.e., estradiol, progesterone and their reduced metabolites) as naturally occur during premenstrual period, post-partum period, and transition to menopause [93, 94]. However, it also occurs when women are subjected to a surgical procedure to remove the ovaries (i.e., oophorectomy) with or without the uterus (i.e., hysterectomy), where an abrupt reduction in steroid hormones concentrations occurs [95] affecting behavioral response. Apparently, the significant reduction of steroid concentration produces anatomical, physiological, and neurochemical changes in the brain, that negatively impact on behavior, emotional, and affective states [96, 97].
\nPreclinical research with laboratory animals has made possible identify the behavioral and emotional changes associated with a reduced concentration of steroid hormones when rats are undergoing to an extirpation of both ovaries (i.e., ovariectomy), which increases vulnerability to stress that can be reverted by injection of severe doses of estradiol [22]. The long-term ovariectomy (> 8 weeks post-ovariectomy) is considered then as a surgical menopause model that explores the behavioral, neurobiological, emotional and affective changes associated with oophorectomy that occurs in women [98]. In the long-term ovariectomized rats display higher anxiety- and depression-like behavior in experimental models such as elevated plus maze and forced swim test, respectively. These behavioral changes are correlated with a reduced neurochemical activity on serotonergic, noradrenergic, dopaminergic, and GABAergic pathways; in addition to a reduction in the number of dendritic spines and neuronal activity in some brain structures (i.e., hippocampus, amygdala, lateral septum, prefrontal cortex, among others). Through behavioral analysis is possible identifying the gradual changes associated with surgical menopause in rats. It was observed that after 3-week post-ovariectomy, rats showed high anxiety-like behavior (i.e., there is a reduction of exploration of the open arms) in the elevated plus maze with respect to cycling rats with intact ovaries, but after 6-week post-ovariectomy, additionally to anxiety-like behavior, rats also displayed high depression-like behavior in the forced swim test (i.e., increase in the total time of immobility), which negatively correlates with the Fos-immunoreactive cells in limbic brain structures such as the lateral septal nucleus [25]. The behavioral and neurochemical characterization of long-term ovariectomy allows the pharmacological research of different substances that could be potentially relevant to the development of pharmacological therapies to ameliorate anxiety and depression symptoms that occur during natural or surgical menopause.
\nAs mentioned before, anxiety-like behavior is dependent on the post-ovariectomy time frame in rats. After 12-weeks post ovariectomy rats show high anxiety-like behavior respect to rats at 3-weeks post-ovariectomy in the burying behavior parading. This high anxiety-like behavior is reduced after injection of 1–2 mg/kg diazepam, a typical anxiolytic benzodiazepine drug [23]. Similarly, i.p. injection of 0.5 and 1 mg/kg phytoestrogen genistein (a secondary metabolite obtained from soybeans) significantly reduces anxiety-like behavior in rats at 12-week post-ovariectomy in the light/dark behavioral paradigm through action on the estrogen receptor-β [99, 100]. Additionally, s.c. injection of 0.9 or 0.18 mg/kg genistein exerts similar anxiolytic-like effects in the elevated plus maze than 17β-estradiol in rats subjected to surgical menopausal model. This is consistent with clinical observations that estradiol reduces anxiety symptoms associated with natural and surgical menopause, and additionally supports the potential use of phytoestrogens as an alternative therapy to ameliorate emotional symptoms associated to menopause.
\nResearch in behavioral pharmacology has contributed to the study of pharmacological actions of natural products. In rats at 12-weeks post-ovariectomy, 50 mg/kg by oral rout of the aqueous crude extract of Montanoa tomentosa, a Mexican plant traditionally recommended for the treatment of anxiety and other illness of women, reduces anxiety-like behavior in the elevated plus maze [101]. Said actions have been related with pharmacological actions on the GABAA receptors [102]. Additionally, secondary metabolites from plants, for example, the flavonoids are reported with anxiolytic properties in behavioral models in rats. In this way, 2 and 4 mg/kg, i.p., of the flavonoid chrysin produces anxiolytic-like effects in rats with surgical menopause subjected to the elevated plus maze and the light/dark test [103]; the said effects were produced through action on the GABAA receptor because the pretreatment with 1 mg/kg picrotoxin, a noncompetitive antagonist of the GABAA receptor, cancels the anxiolytic-like effect of chrysin.
\nAs mentioned before, behavioral pharmacology is an interdisciplinary field. The present chapter tried to reflect briefly the essence of behavioral pharmacology through an anecdotical review of its developments in areas familiar to the authors. All findings mentioned above underline the importance of the research in behavioral pharmacology on the understanding of the neurobiology of different disorders and the mechanism of action of drugs used to treat such disorders, and at the same time, provide a perspective on the current research done in this growing area, which is and will be a cornerstone in the understanding of human behavior and mental health.
\nThe authors do not have any conflict of interest.
Stating that statistical methods are useful in machine learning is analogous to saying that wood working methods are helpful for a carpenter. Statistics is the foundation of machine learning. However not all machine learning methods have been said to have derived from statistics. To begin with let us take a look at what statistics and machine learning means.
Statistics is extensively used in areas of science and finance and in the industry. Statistics is known to be mathematical science and not just mathematics. It is said to have been originated in seventeenth century. It consists of data collection, organizing the data, analyzing the data, interpretation and presentation of data. Statistical methods are being used since a long time in various fields to understand the data efficiently and to gain an in-depth analysis of the data [1].
On the other hand, machine learning is a branch of computer science which uses statistical abilities to learn from a particular dataset [2]. It was invented in the year 1959. It learns using algorithm and then has the ability to predict based on what it has been fed with. Machine learning gives out detailed information than statistics [3].
Most of the techniques of machine learning derive their behavior from statistics. However not many are familiar with this since both of them have their own jargons. For instance learning in statistics is called as fitting, supervised learning from machine learning is called as regression. Machine learning is a subfield of computer science and artificial intelligence. Machine learning is said to be a subdivision of computer science and artificial intelligence. It does use fewer assumptions than statistics. Machine learning unlike statistics deals with large amount of data and it also requires minimum human effort since most of its computation is done by the machine or the computer itself. Machine learning unlike statistics has a strong predicting power than statistics. Depending on the type of data machine learning can be categorized into supervised machine learning, unsupervised machine learning and reinforcement learning [4].
There seems to be analogy between machine learning and statistics. The following picture from textbook shows how statistics and machine learning visualize a model. Table 1 shows how terms of statistics have been coined in machine learning.
Machine learning | Statistics |
---|---|
Network, graphs | Model |
Weights | Parameters |
Learning | Fitting |
Generalization | Tool set performance |
Supervised learning | Regression/classification |
Unsupervised learning | Density estimation, clustering |
Machine learning jargons and corresponding statistics jargons.
To understand how machine learning and statistics come out with the results let’s look at Figure 1. In statistical modeling on the left half of the image, linear regression with two variables is fitting the best plane with fewer errors. In machine learning the right half of the image to fit the model in the best possible way the independent variables have been converted into the square of error terms. That is machine learning strives to get a better fit than the statistical model. In doing so, machine learning minimizes the errors and increases the prediction rates.
Statistical and machine learning method.
Statistics methods are not just useful in training the machine learning model but they are helpful in many other stages of machine learning such as:
Data preparation—where statistics is used for data preprocessing which is later sent to the model. For instance when there are missing values in the dataset, we compute statistical mean or statistical median and fill it in the empty spaces of the dataset. It is recommended that machine learning model should never be fed with a dataset which has empty cells in it. It also used in preprocessing stage to scale the data by which the values are scaled to a particular range by which the mathematical computation becomes easy during the training of machine learning.
Model evaluation—no model is perfect in predicting when it is built for the first time. Simply building the model is not enough. It is vital to check how well is it performing and if not then by how much is it closer to being accurate enough. Hence, we evaluate the model by statistical methods, which tell by how much the result is accurate and a lot many things about the end result obtained. We make use of metrics such as confusion matrix, Kolmogorov Smirnov chart, AUC—ROC, root mean squared error and many metrics to enhance our model.
Model selection—we make use of many algorithms to train the algorithm and there is a chance of selecting only one which gives out accurate results when compared to others. The process of selecting the right solution for this is called model selection. Two of the statistical methods can be used to select the appropriate model such as statistical hypothesis test and estimation statistics [5].
Data selection—some datasets carry a lot of features with them. Of many features, it may happen so that only some contribute significantly in estimation of the result. Considering all the features becomes computationally expensive and as well as time consuming. By making use of statistics concepts we can eliminate the features which do not contribute significantly in producing the result. That is it helps in finding out the dependent variables or features for any result. But it is important to note that this method requires careful and skilled approach. Without which it may lead to wrong results.
In this chapter we take a look at how statistical methods such as, regression and classification are used in machine learning with their own merits and demerits.
Regression is a statistical measure used in finance, investing and many other areas which aims to determine relationship between the dependent variables and ‘n’ number of independent variables. Regression consists of two types:
Linear regression—where one independent variable is used to explain or predict the outcome of the dependent variable.
Multiple regression—where two or more independent variables are used to explain or predict the outcome of the dependent variable.
In statistical modeling, regression analysis consists of set of statistical methods to estimate how the variables are related to each other.
Linear and logistic are the types of regression which are used in predictive modeling [6].
Linear assumes that the relationship between the variables are linear that is they are linearly dependent. The input variables consist of variables X1, X2, …, Xn (where n is a natural number).
Linear models were developed long time ago but till date they are able to produce significant results. That is even in the modern computer’s era they are well off. They are widely used because they are not complex in nature. In prediction, they can even out perform complex nonlinear models.
There are ‘n’ number of regressions that can be performed. We look at the most widely used five types of regression techniques. They are:
Linear regression
Logistic regression
Polynomial regression
Stepwise regression
Ridge regression
Any regression method would involve the following:
The unknown variables is denoted by beta
The dependent variables also known as output variable
The independent variables also known as input variables
It is denoted in the form of function as:
It is the most widely used regression type by far. Linear regression establishes a relationship between the input variables (independent variables) and the output variable (dependent variable).
It assumes that the output variable is a combination of the input variables. A linear regression line is represented by Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is ‘b’, and ‘a’ is the intercept (the value of y when x = 0).
A line regression is represented by the equation:
where X indicates independent variables and ‘Y’ is the dependent variable [7]. This equation when plotted on a graph is a line as shown below in Figure 2.
Linear regression on a dataset.
However, linear regression makes the following assumptions:
That there is a linear relationship
There exists multivariate normality
There exists no multi collinearity or little multicollinearity among the variables
There exists no auto-correlation between the variables
No presence of homoscedasticity
It is fast and easy to model and it is usually used when the relationship to be modeled is not complex. It is easy to understand. However linear regression is sensitive to outliners.
Note: In all of the usages stated in this chapter, we have assumed the following:
The dataset has been divided into training set (denoted by X) and test set (denoted by y_test)
The regression object “reg” has been created and exists.
We have used the following libraries:
Scipy and Numoy for numerical calculations
Pandas for dataset handling
Scikit-learn to implement the algorithm, to split the dataset and various other purposes.
Usage of linear regression in python:
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
#Declare the linear regression function
reg=linear_model.LinearRegression()
#call the method
reg.fit(height,weight)
#to check slope and intercept
m=reg.coef_[0]
b=reg.intercept_
print("slope=",m, "intercept=",b)
# check the accuracy on the training set
reg.score(X, y)
Logistic regression is used when the dependent variable is binary (True/False) in nature. Similarly the value of y ranges from 0 to 1 (Figure 3) and it is represented by the equation:
Standard logistic function.
Logistic regression is used in classification problems. For example to classify emails as spam or not and to predict whether the tumor is malignant or not. It is not mandatory that the input variables have linear relationship to the output variable [8]. The reason being that it makes us of nonlinear log transformation to the predicted odds. It is advised to make use of only the variables which are powerful predictors to increase the algorithms performance.
However, it is important to note the following while making use of logistic regression:
Doesn’t handle large number of categorical features.
The non-linear features should be transformed before using them.
Usage of logistic regression in python:
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
# instantiate a logistic regression model, and fit with X and y
reg = LogisticRegression()
reg = model.fit(X, y)
# check the accuracy on the training set
reg.score(X, y)
It is a type of regression where the independent variable power is greater than 1. Example:
The plotted graph is usually a curve in nature as shown in Figure 4.
Plotted graph is looks as curve in nature.
If the degree of the equation is 2 then it is called quadratic. If 3 then it is called cubic and if it is 4 it is called quartic. Polynomial regressions are fit with the method of least squares. Since the least squares minimizes the variance of the unbiased estimators of all the coefficients which are done under the conditions of Gauss-Markov theorem. Although we may get tempted to fit a higher degree polynomial so that we could get a low error, it may cause over-fitting [9].
Some guidelines which are to be followed are:
The model is more accurate when it fed with large number of observations.
Not a good thing to extrapolate beyond the limits of the observed values.
Values for the predictor shouldn’t be large else they will cause overflow with higher degree.
Usage of polynomial regression in python:
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
#makes use of a pre-processor called degree for the function
reg = PolynomialFeatures(degree=2)
reg.fit_transform(X)
reg.score(X, y)
This type of regression is used when we have multiple independent variables. To select the variables which are independent an automatic process is used. If used in the right way it puts more power and presents us ton of information. It can be used when the number of variables is too many. However if it is used haphazardly it may affect the models performance.
We make use of the following scores to help us find out the independent variables which contribute to the output variable significantly—R-squared, Adj. R-squared, F-statistic, Prob (F-statistic), Log-Likelihood, AIC, BIC and many more.
It can be performed by any of the following ways:
Forward selection—where we start by adding the variables to the set and check how affects the scores.
Backward selection—we start by taking all the variables to the set and start eliminating them one by one by looking at the score after each elimination.
Bidirectional selection—a combination of both the methods mentioned above.
The greatest limitation of using step-wise regression is that the each instance or sample must have at least five attributes. Below which it has been observed that the algorithm doesn’t perform well [10].
Code to implement Backward Elimination algorithm:
Assume that the dataset consists of 5 columns and 30 rows, which are present in the variable ‘X’ and let the expected results contain in the variable ‘y’. Let ‘X_opt’ contain the independent variables which are used to determine the value of ‘y’.
We are making use of a package called statsmodels, which is used to estimate the model and to perform statistical tests.
#import stats models package
import statsmodels.formula.api as sm
#since it is a polynomial add a column of 1s to the left
X = np.append (arr = np.ones([30,1]).astype(int), values = X, axis = 1)
#Let X-opt contain the independent variables only and Let y contain the output variable
X_opt = X[:,[0,1,2,3,4,5]]
#assign y to endog and X_opt to exog
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()
The above code outputs the summary and based on it the variable which should be eliminated should be decided. Once decided remove the variable from ‘X-opt’.
It is used to handle high dimensionality of the dataset.
It can be used to analyze the data in detail. It is a technique which is used to get rid of multi collinearly. That is the independent values may be highly correlated. It adds a degree of bias due to which it reduces the standard errors.
The multi collinearity of the data can be inspected by correlation matrix. Higher the values, more the multi collinearity. It can also be used when number of predictor variables in the dataset exceeds the number of instances or observations [11].
The equation for linear regression is
This equation also contains error. That is it can be expressed as
Error with mean zero and known variance.
Ridge regression is known to shrink the size by imposing penalty on the size. It is also used to control the variance.
In (Figure 5) how ridge regression looks geometrically.
Ridge and OLS.
Usage of ridge regression in python:
from sklearn import linear_model
reg = linear_model.Ridge (alpha = .5)
reg.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,
normalize=False, random_state=None, solver=\'auto\', tol=0.001)
#to return the co-efficient and intercept
reg.coef_
reg.intercept_
Least absolute shrinkage and selection operator is also known as LASSO. Lasso is a linear regression that makes use of shrinkage. It does so by shrinking the data values toward the mean or a central point. This is used when there are high levels of multi collinearity [12].
It is similar to ridge regression and in addition it can reduce the variability and improves the accuracy of linear regression models.
It is used for prostate cancer data analysis and other cancer data analysis.
Important points about LASSO regression:
It helps in feature extraction by shrinking the co-efficient to zero.
It makes use of L1 regularization.
In the data if the predictors are have high correlation, the algorithm selects only one of the predictors discards the rest.
Code to implement in python:
from sklearn import linear_model
clf = linear_model.Lasso(alpha = 0.1)
clf.fit()
Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection=\'cyclic\', tol=0.0001, warm_start=False)
#to return the co-efficent and intercept
print(clf.coef_)
print(clf.intercept_)
A classification task is when the output is of the type “category” such as segregating data with respect to some property. In machine learning and statistics, classification consists of categorizing the new data to a particular category where it fits in on the basis of the data which has been used to train the model. Examples of tasks which make use of classification techniques are classifying emails as spam or not, detecting a disease on plants, predicting whether it will rain on some particular day, predicting the house prices based on the area it is located.
In terms of machine learning classification techniques fall under supervised learning [13].
The categories may be either:
categorical (example: blood groups of humans—A, B, O)
ordinal (example: high, medium or low)
integer valued (example: occurrence of a letter in a sentence)
Real valued
The algorithms which make use of this concept in machine learning and classify the new data are called as “Classifiers.” Algorithms always return a probability score of belonging to the class of interest. That is considered an example where we are required to classify a gold ornament. Now when we input the image to the machine learning model the algorithms returns the probability value for each category, such as for if it is a ring the probability value may be higher than 0.8 if it not a necklace it may return less than 0.2, etc.
Higher the value more likely it is for it to belong to the particular group.
We make use of the following approach to build a machine learning classifier:
Pick a cut off probability above which we consider a record to belong to that class.
Estimate that a new observation belongs to a class.
If the obtained probability is above the cut off probability, assign the new observation to that class.
Classifiers are of two types: linear and nonlinear classifiers.
We now take a look at various classifiers are also statistical techniques:
Naive Bayes
stochastic gradient dissent (SGD)
K-nearest neighbors
decision trees
random forest
support vector machine
In machine learning, these classifiers belong to “probabilistic classifiers.” This algorithm makes use of Bayes’ theorem with strong independence assumptions between the features. Although Naive Bayes were introduced in the early 1950s, they are still being used today [14].
Given a problem instance to be classified, represented by a vector
Which represent ‘n’ features.
We can observe that in the above formula that if the number of features is more or if a feature accommodates a large number of values, then it becomes infeasible. Therefore we rewrite the formula based on Bayes theorem as:
Makes two “naïve” assumptions over attributes:
All attributes are a priori equally important
All attributes are statistically independent (value of one attribute is
not related to a value of another attribute)
This classifier makes two assumptions:
All attributes are equally important
All attributes are not related to another attribute
There are three types of naive Bayes algorithms, which can be used: GaussianNB, BernoulliNB, and MultinomialNB.
Usage of naive Bayes in python:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
reg= GaussianNB()
reg.fit(X,y)
reg.predict(X_test)
reg.score()
An example of linear classifier which implements regularized linear model (Figure 6) with stochastic gradient dissent. Stochastic gradient descent (often shortened to SGD), also known as incremental gradient descent, is an iterative method to optimize a differentiable objective function, a stochastic approximation of gradient descent optimization [15]. Although SGD has been a part of machine learning since ages it wasn’t extensively used until recently.
In linear regression algorithm, we make us of least squares to fit the line. To ensure that the error is low we use gradient descent. Although gradient descent does the job it can’t handle big tasks hence we use stochastic gradient classifier. SGD calculates the derivative of each training data and also calculates the update within no time.
The advantages of using SGD classifier are that they are efficient and they are easy to implement.
Feature scaling classifier.
However it is sensitive to feature scaling.
Usage of SGD classifier:
from sklearn.linear_model import SGDClassifier
X = [[0., 0.], [1., 1.]]
y = [0, 1]
clf = SGDClassifier (loss = "hinge", penalty = "l2")
clf.fit(X, y)
#to predict the values
clf.fit(X_test)
Also known as k-NN is a method used to classify as well as for regression. The input consists of k number of closest training examples. It is also referred as lazy learning since the training phase doesn’t require a lot of effort.
In k-NN an object’s classification is solely dependent on the majority vote of the object’s neighbors. That is the outcome is based on the presence of the neighbors. The object is assigned to the class most common among its k nearest neighbors. If the value of k is equal to 1 then it’s assigned to its nearest neighbor. Simply put, the k-NN algorithm is entirely dependent on the neighbors of the object to be classified. Greater the influence of a neighbor, the object is assigned to it. It is termed as simplest machine learning algorithm among all the algorithms [16].
Let us consider an example where the green circle is the object which is to be classified as shown in Figure 7. Let us assume that there are two circles—the solid circle and the dotted circle.
K-Neighbors.
As we know that there are two classes class 1 (blue squares) and class 2 (red squares). If we consider only the inner circle that is the solid circle then there are two objects of red circle existing which dominates the number of blue squares due to which the new object is classified to Class 1. But if we consider the dotted circle, the number of blue circle dominates since there are more number of blue squares due to which the object is classified to Class 2 [17].
However, the cost of learning process is zero.
The algorithm may suffer from curse of dimensionality since the number of dimensions greatly affects its performance. When the dataset is very large the computation becomes very complex since the algorithm takes time to look out for its neighbors. If there are many dimensions then the samples nearest neighbors can be far away. To avoid curse of dimensionality dimension reduction is usually performed before applying k-NN algorithm to the data.
Also the algorithm may not perform well with categorical data since it is difficult to find the distance between the categorical features.
Usage in python:
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier (n_neighbors=5)
classifier.fit(X_train, y_train)
Decision trees are considered to be most popular classification algorithms while classifying data. Decision trees are a type of supervised algorithm where the data is split based on certain parameters. The trees consist of decision nodes and leaves [18].
The decision tree consists of a root tree from where the tree generates and this root tree doesn’t have any inputs. It is the point from which the tree originates. All the other nodes except the root node have exactly one incoming node. The other nodes except the root node are called leaves. Below is the example of a decision tree an illustration of how the decision tree looks like as shown in Figure 8.
Typical decision tree.
“Is sex male” is the root node from where the tree originates. Depending on the condition the tree further bifurcates into subsequent leaf nodes. Few more conditions like “is Age >9.5?” are applied by which the depth of the node goes on increasing. As the number of leaf nodes increase the depth of the tree goes on increasing. The leaf can also hold a probability vector.
Decision tree algorithms implicitly construct a decision tree for any dataset.
The goal is to construct an optimal decision tree by minimalizing the generalization error. For any tree algorithm, it can be tuned by making changes to parameters such as “Depth of the tree,” “Number of nodes,” “Max features.” However construction of a tree by the algorithm can get complex for large problems since the number of nodes increase as well as the depth of the tree increases.
Advantages of this tree are that they are simple to understand and can be easily interpreted. It also requires little data preparation. The tree can handle both numerical and categorical data unlike many other algorithms. It also easy to validate the decision tree model using statistical testes. However, disadvantages of the trees are that they can be complex in nature for some cases which won’t generalize the data well. They are unstable in nature since if there are small variations in data they may change the structure of the tree completely.
Usage in python:
from sklearn.neighbors import tree
classifier = tree.DecisionTreeClassifier()
classifier.fit(X_train, y_train)
clf.predict(X_test)
These are often referred as ensemble algorithms since these algorithms combine the use of two or more algorithms. They are improved version of bagged decision trees. They are used for classification, regression, etc.
Random forest creates n number of decision trees from a subset of the data. On creating the trees it aggregates the votes from the different trees and then decides the final class of the sample object. Random forest is used in recommendation engines, image classification and feature selection [19].
The process consists of four steps:
It selects random samples from the dataset.
For every dataset construct a dataset and then predict from every decision tree.
For every predicted result perform vote.
Select the prediction which has the highest number of votes.
Random forest’s default parameters often produce a good result in most of the cases. Additionally, one can make changes to achieve desired results. The parameters in Random Forest which can be used to tune the algorithm which can be used to give better and efficient results are:
Increasing the predictive power by increasing “n_estimators” by which the number of tress which will be built can be altered. “max_features” parameter can also be adjusted which is the number of features which are used to train the algorithm. Another parameter which can be adjusted is “min_sample_leaf” which is the number of leafs that are used to split the internal node.
To increase the model’s speed, “n_jobs” parameter can be adjusted which is the number of processors it can use. To use as many as needed “−1” can be specified which signifies that there is no limit.
Due to large number of decision trees random forest is highly accurate. Since it takes the average of all the predictions which are computed the algorithm doesn’t suffer from over fitting. Also it does handle missing values from the dataset. However, the algorithm is takes time to compute since it takes time to build trees and take the average of the predictions and so on.
One of the real time examples where random forest algorithm can be used is predicting a person’s systolic blood pressure based on the person’s height, age, weight, gender, etc.
Random forests require very little tuning when compared to other algorithms. The main disadvantage of random forest algorithm is that increased number of tress can make the process computationally expensive and lead to inaccurate results.
Usage in python:
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X, y)
clf.predict(X_test)
Support vector machines also known as SVMs or support vector networks fall under supervised learning. They are used for classification as well as regression purposes. Support vectors are the data points which lie close to the hyper plane. When the data is fed to the algorithm the algorithm builds a classifier which can be used to assign new examples to one class or the other [20]. A SVM consists of points in space separated by a gap which is as wide as possible. When a new sample is encountered it maps it to the corresponding category.
Perhaps when the data is unlabeled it becomes difficult for the supervised SVM to perform and this is where unsupervised method of classifying is required.
A SVM constructs a hyper plane which can be used for classification, regression and many other purposes. A good separation can be achieved when the hyper plane has the largest distance to the nearest training point of a class.
In (Figure 9) H1 line doesn’t separate, while H2 separates but the margin is very small whereas H3 separates such as the distance between the margin and the nearest point is maximum when compared to H1 and H2.
Hyper plane construction and H1, H2 and H3 line separation.
SVMs can be used in a variety of applications such as:
They are used to categorize text, to classify images, handwritten images can be recognized, and they are also used in the field of biology.
SVMs can be used with the following kernels:
Polynomial kernel SVM
Linear kernel SVM
Gaussian kernel SVM
Gaussian radial basis function SVM (RBF)
The advantages of SVM are:
Effective in high dimensional data
It is memory efficient
It is versatile
It may be difficult for SVM to classify at times due to which the decision boundary is not optimal. For example, when we want to plot the points randomly distributed on a number line.
It is almost impossible to separate them. So in such cases we transform the dataset by applying 2D or 3D transformations by using a polynomial function or any other appropriate function. By doing so it becomes easier to draw a hyper plane.
When the number of features is much greater than number of samples it doesn’t perform well with the default parameters.
Usage of SVM in python:
from sklearn import svm
clf = svm.SVC()
clf.fit(X,y)
clf.predict(X_test)
It is evident from the above regression and classification techniques are strongly influenced by statistics. The methods have been derived from statistical methods which existed since a long time. Statistical methods also consist of building models which consists of parameters and then fitting it. However not all the methods which are being used derive their nature from statistics. Not all statistical methods are being used in machine learning. Extensive research in the field of statistical methods may give out new set methods which can be used in machine learning apart from the existing statistical methods which are being used today. It can also be stated that machine learning to some extent is a form of ‘Applied Statistics.’
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\n\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
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\n\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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