Bone cells, their function, and locations [1, 2, 3, 4, 5, 6, 7].
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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His research areas include security in wired and wireless networks, intrusion detection systems, secure routing protocols in wireless ad hoc and sensor networks, secure multicast and broadcast communication in next-generation broadband wireless networks, trust- and reputation-based systems, quality of service in multimedia communication in wireless networks and cross-layer optimization-based resource allocation algorithms in next-generation wireless networks, sensor networks, and privacy issues in ubiquitous and pervasive communication, big data analytics, R, Python, Spark, Hadoop and MapReduce programming. Currently, he is active in the fields of applied statistical modelling, data mining and machine learning, data warehousing, and multi-dimensional modelling, social media and mobile analytics, artificial intelligence, and deep learning. Prof. Sen has more than 200 publications in international journals and referred conference proceedings (IEEE Xplore, ACM Digital Library, Springer LNCS etc.), and seventeen chapters in books. He has delivered expert talks and keynote lectures at various international conferences and symposia. He is a senior member of ACM, a member of IEEE, and a lifetime member of Indian Society of Technical Education (ISTE), and was also an active member of the security group of IEEE 802.16 standard body and European Telecommunication Standards Institute (ETSI). His biography has been listed in Marquis Who’s Who in the World annually since 2008. He has delivered invited talks at many prestigious international conferences both in India and abroad and has conducted a number of training programs for teachers of Computer Science and Engineering and Data Science at various universities. 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Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"64747",title:"Bone Development and Growth",doi:"10.5772/intechopen.82452",slug:"bone-development-and-growth",body:'Bone is living tissue that is the hardest among other connective tissues in the body, consists of 50% water. The solid part remainder consisting of various minerals, especially 76% of calcium salt and 33% of cellular material. Bone has vascular tissue and cellular activity products, especially during growth which is very dependent on the blood supply as basic source and hormones that greatly regulate this growth process. Bone-forming cells, osteoblasts, osteoclast play an important role in determining bone growth, thickness of the cortical layer and structural arrangement of the lamellae.
Bone continues to change its internal structure to reach the functional needs and these changes occur through the activity of osteoclasts and osteoblasts. The bone seen from its development can be divided into two processes: first is the intramembranous ossification in which bones form directly in the form of primitive mesenchymal connective tissue, such as the mandible, maxilla and skull bones. Second is the endochondral ossification in which bone tissue replaces a preexisting hyaline cartilage, for example during skull base formation. The same formative cells form two types of bone formation and the final structure is not much different.
Bone growth depends on genetic and environmental factors, including hormonal effects, diet and mechanical factors. The growth rate is not always the same in all parts, for example, faster in the proximal end than the distal humerus because the internal pattern of the spongiosum depends on the direction of bone pressure. The direction of bone formation in the epiphysis plane is determined by the direction and distribution of the pressure line. Increased thickness or width of the bone is caused by deposition of new bone in the form of circumferential lamellae under the periosteum. If bone growth continues, the lamella will be embedded behind the new bone surface and be replaced by the haversian canal system.
Bone is a tissue in which the extracellular matrix has been hardened to accommodate a supporting function. The fundamental components of bone, like all connective tissues, are cells and matrix. Although bone cells compose a small amount of the bone volume, they are crucial to the function of bones. Four types of cells are found within bone tissue: osteoblasts, osteocytes, osteogenic cells, and osteoclasts. They each unique functions and are derived from two different cell lines (Figure 1 and Table 1) [1, 2, 3, 4, 5, 6, 7].
Osteoblast synthesizes the bone matrix and are responsible for its mineralization. They are derived from osteoprogenitor cells, a mesenchymal stem cell line.
Osteocytes are inactive osteoblasts that have become trapped within the bone they have formed.
Osteoclasts break down bone matrix through phagocytosis. Predictably, they ruffled border, and the space between the osteoblast and the bone is known as Howship’s lacuna.
Development of bone precursor cells. Bone precursor cells are divided into developmental stages, which are 1. mesenchymal stem cell, 2. pre-osteoblast, 3. osteoblast, and 4. mature osteocytes, and 5. osteoclast.
The balance between osteoblast and osteoclast activity governs bone turnover and ensures that bone is neither overproduced nor overdegraded. These cells build up and break down bone matrix, which is composed of:
Osteoid, which is the unmineralized matrix composed of type I collagen and gylcosaminoglycans (GAGs).
Calcium hydroxyapatite, a calcium salt crystal that give bone its strength and rigidity.
Bone is divided into two types that are different structurally and functionally. Most bones of the body consist of both types of bone tissue (Figure 2) [1, 2, 8, 9]:
Compact bone, or cortical bone, mainly serves a mechanical function. This is the area of bone to which ligaments and tendons attach. It is thick and dense.
Trabecular bone, also known as cancellous bone or spongy bone, mainly serves a metabolic function. This type of bone is located between layers of compact bone and is thin porous. Location within the trabeculae is the bone marrow.
Structure of a long bone.
Long bones are composed of both cortical and cancellous bone tissue. They consist of several areas (Figure 3) [3, 4]:
The epiphysis is located at the end of the long bone and is the parts of the bone that participate in joint surfaces.
The diaphysis is the shaft of the bone and has walls of cortical bone and an underlying network of trabecular bone.
The epiphyseal growth plate lies at the interface between the shaft and the epiphysis and is the region in which cartilage proliferates to cause the elongation of the bone.
The metaphysis is the area in which the shaft of the bone joins the epiphyseal growth plate.
Bone macrostructure. (a) Growing long bone showing epiphyses, epiphyseal plates, metaphysis and diaphysis. (b) Mature long bone showing epiphyseal lines.
Different areas of the bone are covered by different tissue [4]:
The epiphysis is lined by a layer of articular cartilage, a specialized form of hyaline cartilage, which serves as protection against friction in the joints.
The outside of the diaphysis is lined by periosteum, a fibrous external layer onto which muscles, ligaments, and tendons attach.
The inside of the diaphysis, at the border between the cortical and cancellous bone and lining the trabeculae, is lined by endosteum.
Compact bone is organized as parallel columns, known as Haversian systems, which run lengthwise down the axis of long bones. These columns are composed of lamellae, concentric rings of bone, surrounding a central channel, or Haversian canal, that contains the nerves, blood vessels, and lymphatic system of the bone. The parallel Haversian canals are connected to one another by the perpendicular Volkmann’s canals.
The lamellae of the Haversian systems are created by osteoblasts. As these cells secrete matrix, they become trapped in spaces called lacunae and become known as osteocytes. Osteocytes communicate with the Haversian canal through cytoplasmic extensions that run through canaliculi, small interconnecting canals (Figure 4) [1, 2, 8, 9]:
Bone microstructure. Compact and spongy bone structures.
The layers of a long bone, beginning at the external surface, are therefore:
Periosteal surface of compact bone
Outer circumferential lamellae
Compact bone (Haversian systems)
Inner circumferential lamellae
Endosteal surface of compact bone
Trabecular bone
Bone development begins with the replacement of collagenous mesenchymal tissue by bone. This results in the formation of woven bone, a primitive form of bone with randomly organized collagen fibers that is further remodeled into mature lamellar bone, which possesses regular parallel rings of collagen. Lamellar bone is then constantly remodeled by osteoclasts and osteoblasts. Based on the development of bone formation can be divided into two parts, called endochondral and intramembranous bone formation/ossification [1, 2, 3, 8].
During intramembranous bone formation, the connective tissue membrane of undifferentiated mesenchymal cells changes into bone and matrix bone cells [10]. In the craniofacial cartilage bones, intramembranous ossification originates from nerve crest cells. The earliest evidence of intramembranous bone formation of the skull occurs in the mandible during the sixth prenatal week. In the eighth week, reinforcement center appears in the calvarial and facial areas in areas where there is a mild stress strength [11].
Intramembranous bone formation is found in the growth of the skull and is also found in the sphenoid and mandible even though it consists of endochondral elements, where the endochondral and intramembranous growth process occurs in the same bone. The basis for either bone formation or bone resorption is the same, regardless of the type of membrane involved.
Sometimes according to where the formation of bone tissue is classified as “periosteal” or “endosteal”. Periosteal bone always originates from intramembranous, but endosteal bone can originate from intramembranous as well as endochondral ossification, depending on the location and the way it is formed [3, 12].
The statement below is the stage of intramembrane bone formation (Figure 5) [3, 4, 11, 12]:
An ossification center appears in the fibrous connective tissue membrane. Mesenchymal cells in the embryonic skeleton gather together and begin to differentiate into specialized cells. Some of these cells differentiate into capillaries, while others will become osteogenic cells and osteoblasts, then forming an ossification center.
Bone matrix (osteoid) is secreted within the fibrous membrane. Osteoblasts produce osteoid tissue, by means of differentiating osteoblasts from the ectomesenchyme condensation center and producing bone fibrous matrix (osteoid). Then osteoid is mineralized within a few days and trapped osteoblast become osteocytes.
Woven bone and periosteum form. The encapsulation of cells and blood vessels occur. When osteoid deposition by osteoblasts continues, the encased cells develop into osteocytes. Accumulating osteoid is laid down between embryonic blood vessels, which form a random network (instead of lamellae) of trabecular. Vascularized mesenchyme condenses on external face of the woven bone and becomes the periosteum.
Production of osteoid tissue by membrane cells: osteocytes lose their ability to contribute directly to an increase in bone size, but osteoblasts on the periosteum surface produce more osteoid tissue that thickens the tissue layer on the existing bone surface (for example, appositional bone growth). Formation of a woven bone collar that is later replaced by mature lamellar bone. Spongy bone (diploe), consisting of distinct trabeculae, persists internally and its vascular tissue becomes red marrow.
Osteoid calcification: The occurrence of bone matrix mineralization makes bones relatively impermeable to nutrients and metabolic waste. Trapped blood vessels function to supply nutrients to osteocytes as well as bone tissue and eliminate waste products.
The formation of an essential membrane of bone which includes a membrane outside the bone called the bone endosteum. Bone endosteum is very important for bone survival. Disruption of the membrane or its vascular tissue can cause bone cell death and bone loss. Bones are very sensitive to pressure. The calcified bones are hard and relatively inflexible.
The stage of intramembranous ossification. The following stages are (a) Mesenchymal cells group into clusters, and ossification centers form. (b) Secreted osteoid traps osteoblasts, which then become osteocytes. (c) Trabecular matrix and periosteum form. (d) Compact bone develops superficial to the trabecular bone, and crowded blood vessels condense into red marrow.
The matrix or intercellular substance of the bone becomes calcified and becomes a bone in the end. Bone tissue that is found in the periosteum, endosteum, suture, and periodontal membrane (ligaments) is an example of intramembranous bone formation [3, 13].
Intramembranous bone formation occurs in two types of bone: bundle bone and lamellar bone. The bone bundle develops directly in connective tissue that has not been calcified. Osteoblasts, which are differentiated from the mesenchyme, secrete an intercellular substance containing collagen fibrils. This osteoid matrix calcifies by precipitating apatite crystals. Primary ossification centers only show minimal bone calcification density. The apatite crystal deposits are mostly irregular and structured like nets that are contained in the medullary and cortical regions. Mineralization occurs very quickly (several tens of thousands of millimeters per day) and can occur simultaneously in large areas. These apatite deposits increase with time. Bone tissue is only considered mature when the crystalized area is arranged in the same direction as collagen fibrils.
Bone tissue is divided into two, called the outer cortical and medullary regions, these two areas are destroyed by the resorption process; which goes along with further bone formation. The surrounding connective tissue will differentiate into the periosteum. The lining in the periosteum is rich in cells, has osteogenic function and contributes to the formation of thick bones as in the endosteum.
In adults, the bundle bone is usually only formed during rapid bone remodeling. This is reinforced by the presence of lamellar bone. Unlike bundle bone formation, lamellar bone development occurs only in mineralized matrix (e.g., cartilage that has calcified or bundle bone spicules). The nets in the bone bundle are filled to strengthen the lamellar bone, until compact bone is formed. Osteoblasts appear in the mineralized matrix, which then form a circle with intercellular matter surrounding the central vessels in several layers (Haversian system). Lamella bone is formed from 0.7 to 1.5 microns per day. The network is formed from complex fiber arrangements, responsible for its mechanical properties. The arrangement of apatites in the concentric layer of fibrils finally meets functional requirements. Lamellar bone depends on ongoing deposition and resorption which can be influenced by environmental factors, one of this which is orthodontic treatment.
Intramembranous bone formation from desmocranium (suture and periosteum) is mediated by mesenchymal skeletogenetic structures and is achieved through bone deposition and resorption [8]. This development is almost entirely controlled through local epigenetic factors and local environmental factors (i.e. by muscle strength, external local pressure, brain, eyes, tongue, nerves, and indirectly by endochondral ossification). Genetic factors only have a nonspecific morphogenetic effect on intramembranous bone formation and only determine external limits and increase the number of growth periods. Anomaly disorder (especially genetically produced) can affect endochondral bone formation, so local epigenetic factors and local environmental factors, including steps of orthodontic therapy, can directly affect intramembranous bone formation [3, 11].
During endochondral ossification, the tissue that will become bone is firstly formed from cartilage, separated from the joint and epiphysis, surrounded by perichondrium which then forms the periosteum [11]. Based on the location of mineralization, it can be divided into: Perichondral Ossification and Endochondral Ossification. Both types of ossification play an essential role in the formation of long bones where only endochondral ossification takes place in short bones. Perichondral ossification begins in the perichondrium. Mesenchymal cells from the tissue differentiate into osteoblasts, which surround bony diaphyseal before endochondral ossification, indirectly affect its direction [3, 8, 12]. Cartilage is transformed into bone is craniofacial bone that forms at the eigth prenatal week. Only bone on the cranial base and part of the skull bone derived from endochondral bone formation. Regarding to differentiate endochondral bone formation from chondrogenesis and intramembranous bone formation, five sequences of bone formation steps were determined [3].
The statements below are the stages of endochondral bone formation (Figure 6) [4, 12]:
Mesenchymal cells group to form a shape template of the future bone.
Mesenchymal cells differentiate into chondrocytes (cartilage cells).
Hypertrophy of chondrocytes and calcified matrix with calcified central cartilage primordium matrix formed. Chondrocytes show hypertrophic changes and calcification from the cartilage matrix continues.
Entry of blood vessels and connective tissue cells. The nutrient artery supplies the perichondrium, breaks through the nutrient foramen at the mid-region and stimulates the osteoprogenitor cells in the perichondrium to produce osteoblasts, which changes the perichondrium to the periosteum and starts the formation of ossification centers.
The periosteum continues its development and the division of cells (chondrocytes) continues as well, thereby increasing matrix production (this helps produce more length of bone).
The perichondrial membrane surrounds the surface and develops new chondroblasts.
Chondroblasts produce growth in width (appositional growth).
Cells at the center of the cartilage lyse (break apart) triggers calcification.
The stage of endochondral ossification. The following stages are: (a) Mesenchymal cells differentiate into chondrocytes. (b) The cartilage model of the future bony skeleton and the perichondrium form. (c) Capillaries penetrate cartilage. Perichondrium transforms into periosteum. Periosteal collar develops. Primary ossification center develops. (d) Cartilage and chondrocytes continue to grow at ends of the bone. (e) Secondary ossification centers develop. (f) Cartilage remains at epiphyseal (growth) plate and at joint surface as articular cartilage.
During endochondral bone formation, mesenchymal tissue firstly differentiates into cartilage tissue. Endochondral bone formation is morphogenetic adaptation (normal organ development) which produces continuous bone in certain areas that are prominently stressed. Therefore, this endochondral bone formation can be found in the bones associated with joint movements and some parts of the skull base. In hypertrophic cartilage cells, the matrix calcifies and the cells undergo degeneration. In cranial synchondrosis, there is proliferation in the formation of bones on both sides of the bone plate, this is distinguished by the formation of long bone epiphyses which only occurs on one side only [2, 14].
As the cartilage grows, capillaries penetrate it. This penetration initiates the transformation of the perichondrium into the bone-producing periosteum. Here, the osteoblasts form a periosteal collar of compact bone around the cartilage of the diaphysis. By the second or third month of fetal life, bone cell development and ossification ramps up and creates the primary ossification center, a region deep in the periosteal collar where ossification begins [4, 10].
While these deep changes occur, chondrocytes and cartilage continue to grow at the ends of the bone (the future epiphyses), which increase the bone length and at the same time bone also replaces cartilage in the diaphysis. By the time the fetal skeleton is fully formed, cartilage only remains at the joint surface as articular cartilage and between the diaphysis and epiphysis as the epiphyseal plate, the latter of which is responsible for the longitudinal growth of bones. After birth, this same sequence of events (matrix mineralization, death of chondrocytes, invasion of blood vessels from the periosteum, and seeding with osteogenic cells that become osteoblasts) occur in the epiphyseal regions, and each of these centers of activity is referred to as a secondary ossification center [4, 8, 10].
There are four important things about cartilage in endochondral bone formation:
Cartilage has a rigid and firm structure, but not usually calcified nature, giving three basic functions of growth (a) its flexibility can support an appropriate network structure (nose), (b) pressure tolerance in a particular place where compression occurs, (c) the location of growth in conjunction with enlarging bone (synchondrosis of the skull base and condyle cartilage).
Cartilage grows in two adjacent places (by the activity of the chondrogenic membrane) and grows in the tissues (chondrocyte cell division and the addition of its intercellular matrix).
Bone tissue is not the same as cartilage in terms of its tension adaptation and cannot grow directly in areas of high compression because its growth depends on the vascularization of bone formation covering the membrane.
Cartilage growth arises where linear growth is required toward the pressure direction, which allows the bone to lengthen to the area of strength and has not yet grown elsewhere by membrane ossification in conjunction with all periosteal and endosteal surfaces.
Membrane disorders or vascular supply problem of these essential membranes can directly result in bone cell death and ultimately bone damage. Calcified bones are generally hard and relatively inflexible and sensitive to pressure [12].
Cranial synchondrosis (e.g., spheno ethmoidal and spheno occipital growth) and endochondral ossification are further determined by chondrogenesis. Chondrogenesis is mainly influenced by genetic factors, similar to facial mesenchymal growth during initial embryogenesis to the differentiation phase of cartilage and cranial bone tissue.
This process is only slightly affected by local epigenetic and environmental factors. This can explain the fact that the cranial base is more resistant to deformation than desmocranium. Local epigenetic and environmental factors cannot trigger or inhibit the amount of cartilage formation. Both of these have little effect on the shape and direction of endochondral ossification. This has been analyzed especially during mandibular condyle growth.
Local epigenetics and environmental factors only affect the shape and direction of cartilage formation during endochondral ossification Considering the fact that condyle cartilage is a secondary cartilage, it is assumed that local factors provide a greater influence on the growth of mandibular condyle.
Chondrogenesis is the process by which cartilage is formed from condensed mesenchyme tissue, which differentiates into chondrocytes and begins secreting the molecules that form the extracellular matrix [5, 14].
The statement below is five steps of chondrogenesis [8, 14]:
Chondroblasts produce a matrix: the extracellular matrix produced by cartilage cells, which is firm but flexible and capable of providing a rigid support.
Cells become embed in a matrix: when the chondroblast changes to be completely embed in its own matrix material, cartilage cells turn into chondrocytes. The new chondroblasts are distinguished from the membrane surface (perichondrium), this will result in the addition of cartilage size (cartilage can increase in size through apposition growth).
Chondrocytes enlarge, divide and produce a matrix. Cell growth continues and produces a matrix, which causes an increase in the size of cartilage mass from within. Growth that causes size increase from the inside is called interstitial growth.
The matrix remains uncalcified: cartilage matrix is rich of chondroitin sulfate which is associated with non-collagen proteins. Nutrition and metabolic waste are discharged directly through the soft matrix to and from the cell. Therefore, blood vessels aren’t needed in cartilage.
The membrane covers the surface but is not essential: cartilage has a closed membrane vascularization called perichondrium, but cartilage can exist without any of these. This property makes cartilage able to grow and adapt where it needs pressure (in the joints), so that cartilage can receive pressure.
Endochondral ossification begins with characteristic changes in cartilage bone cells (hypertrophic cartilage) and the environment of the intercellular matrix (calcium laying), the formation which is called as primary spongiosa. Blood vessels and mesenchymal tissues then penetrate into this area from the perichondrium. The binding tissue cells then differentiate into osteoblasts and cells. Chondroblasts erode cartilage in a cave-like pattern (cavity). The remnants of mineralized cartilage the central part of laying the lamellar bone layer.
The osteoid layer is deposited on the calcified spicules remaining from the cartilage and then mineralized to form spongiosa bone, with fine reticular structures that resemble nets that possess cartilage fragments between the spicular bones. Spongy bones can turn into compact bones by filling empty cavities. Both endochondral and perichondral bone growth both take place toward epiphyses and joints. In the bone lengthening process during endochondral ossification depends on the growth of epiphyseal cartilage. When the epiphyseal line has been closed, the bone will not increase in length. Unlike bone, cartilage bone growth is based on apposition and interstitial growth. In areas where cartilage bone is covered by bone, various variations of zone characteristics, based on the developmental stages of each individual, can differentiate which then continuously merge with each other during the conversion process. Environmental influences (co: mechanism of orthopedic functional tools) have a strong effect on condylar cartilage because the bone is located more superficially [5].
Cartilage bone height development occurs during the third month of intra uterine life. Cartilage plate extends from the nasal bone capsule posteriorly to the foramen magnum at the base of the skull. It should be noted that cartilages which close to avascular tissue have internal cells obtained from the diffusion process from the outermost layer. This means that the cartilage must be flatter. In the early stages of development, the size of a very small embryo can form a chondroskeleton easily in which the further growth preparation occurs without internal blood supply [1].
During the fourth month in the uterus, the development of vascular elements to various points of the chondrocranium (and other parts of the early cartilage skeleton) becomes an ossification center, where the cartilage changes into an ossification center, and bone forms around the cartilage. Cartilage continues to grow rapidly but it is replaced by bone, resulting in the rapid increase of bone amount. Finally, the old chondrocranium amount will decrease in the area of cartilage and large portions of bone, assumed to be typical in ethmoid, sphenoid, and basioccipital bones. The cartilage growth in relation to skeletal bone is similar as the growth of the limbs [1, 3].
Longitudinal bone growth is accompanied by remodeling which includes appositional growth to thicken the bone. This process consists of bone formation and reabsorption. Bone growth stops around the age of 21 for males and the age of 18 for females when the epiphyses and diaphysis have fused (epiphyseal plate closure).
Normal bone growth is dependent on proper dietary intake of protein, minerals and vitamins. A deficiency of vitamin D prevents calcium absorption from the GI tract resulting in rickets (children) or osteomalacia (adults). Osteoid is produced but calcium salts are not deposited, so bones soften and weaken.
At the length of the long bones, the reinforcement plane appears in the middle and at the end of the bone, finally produces the central axis that is called the diaphysis and the bony cap at the end of the bone is called the epiphysis. Between epiphyses and diaphysis is a calcified area that is not calcified called the epiphyseal plate. Epiphyseal plate of the long bone cartilage is a major center for growth, and in fact, this cartilage is responsible for almost all the long growths of the bones. This is a layer of hyaline cartilage where ossification occurs in immature bones. On the epiphyseal side of the epiphyseal plate, the cartilage is formed. On the diaphyseal side, cartilage is ossified, and the diaphysis then grows in length. The epiphyseal plate is composed of five zones of cells and activity [3, 4].
Near the outer end of each epiphyseal plate is the active zone dividing the cartilage cells. Some of them, pushed toward diaphysis with proliferative activity, develop hypertrophy, secrete an extracellular matrix, and finally the matrix begins to fill with minerals and then is quickly replaced by bone. As long as cartilage cells multiply growth will continue. Finally, toward the end of the normal growth period, the rate of maturation exceeds the proliferation level, the latter of the cartilage is replaced by bone, and the epiphyseal plate disappears. At that time, bone growth is complete, except for surface changes in thickness, which can be produced by the periosteum [4]. Bones continue to grow in length until early adulthood. The lengthening is stopped in the end of adolescence which chondrocytes stop mitosis and plate thins out and replaced by bone, then diaphysis and epiphyses fuse to be one bone (Figure 7). The rate of growth is controlled by hormones. When the chondrocytes in the epiphyseal plate cease their proliferation and bone replaces the cartilage, longitudinal growth stops. All that remains of the epiphyseal plate is the epiphyseal line. Epiphyseal plate closure will occur in 18-year old females or 21-year old males.
Oppositional bone growth and remodeling. The epiphyseal plate is responsible for longitudinal bone growth.
The cartilage found in the epiphyseal gap has a defined hierarchical structure, directly beneath the secondary ossification center of the epiphysis. By close examination of the epiphyseal plate, it appears to be divided into five zones (starting from the epiphysis side) (Figure 8) [4]:
The resting zone: it contains hyaline cartilage with few chondrocytes, which means no morphological changes in the cells.
The proliferative zone: chondrocytes with a higher number of cells divide rapidly and form columns of stacked cells parallel to the long axis of the bone.
The hypertrophic cartilage zone: it contains large chondrocytes with cells increasing in volume and modifying the matrix, effectively elongating bone whose cytoplasm has accumulated glycogen. The resorbed matrix is reduced to thin septa between the chondrocytes.
The calcified cartilage zone: chondrocytes undergo apoptosis, the thin septa of cartilage matrix become calcified.
The ossification zone: endochondral bone tissue appears. Blood capillaries and osteoprogenitor cells (from the periosteum) invade the cavities left by the chondrocytes. The osteoprogenitor cells form osteoblasts, which deposit bone matrix over the three-dimensional calcified cartilage matrix.
Epiphyseal plate growth. Five zones of epiphyseal growth plate includes: 1. resting zone, 2. proliferation zone, 3. hypertrophic cartilage zone, 4. calcified cartilage zone, and 5. ossification zone.
When bones are increasing in length, they are also increasing in diameter; diameter growth can continue even after longitudinal growth stops. This is called appositional growth. The bone is absorbed on the endosteal surface and added to the periosteal surface. Osteoblasts and osteoclasts play an essential role in appositional bone growth where osteoblasts secrete a bone matrix to the external bone surface from diaphysis, while osteoclasts on the diaphysis endosteal surface remove bone from the internal surface of diaphysis. The more bone around the medullary cavity is destroyed, the more yellow marrow moves into empty space and fills space. Osteoclasts resorb the old bone lining the medullary cavity, while osteoblasts through intramembrane ossification produce new bone tissue beneath the periosteum. Periosteum on the bone surface also plays an important role in increasing thickness and in reshaping the external contour. The erosion of old bone along the medullary cavity and new bone deposition under the periosteum not only increases the diameter of the diaphysis but also increases the diameter of the medullary cavity. This process is called modeling (Figure 9) [3, 4, 15].
Appositional bone growth. Bone deposit by osteoblast as bone resorption by osteoclast.
Recent research reported that bone microstructure is also the principle of bone function, which regulates its mechanical function. Bone tissue function influenced by many factors, such as hormones, growth factors, and mechanical loading. The microstructure of bone tissue is distribution and alignment of biological apatite (BAp) crystallites. This is determined by the direction of bone cell behavior, for example cell migration and cell regulation. Ozasa et al. found that artificial control the direction of mesenchymal stem cell (MSCs) migration and osteoblast alignment can reconstruct bone microstructure, which guide an appropriate bone formation during bone remodeling and regeneration [16].
Bone development begins with the replacement of collagenous mesenchymal tissue by bone. Generally, bone is formed by endochondral or intramembranous ossification. Intramembranous ossification is essential in the bone such as skull, facial bones, and pelvis which MSCs directly differentiate to osteoblasts. While, endochondral ossification plays an important role in most bones in the human skeleton, including long, short, and irregular bones, which MSCs firstly experience to condensate and then differentiate into chondrocytes to form the cartilage growth plate and the growth plate is then gradually replaced by new bone tissue [3, 8, 12].
MSC migration and differentiation are two important physiological processes in bone formation. MSCs migration raise as an essential step of bone formation because MSCs initially need to migrate to the bone surface and then contribute in bone formation process, although MSCs differentiation into osteogenic cells is also crucial. MSC migration during bone formation has attracted more attention. Some studies show that MSC migration to the bone surface is crucial for bone formation [17]. Bone marrow and periosteum are the main sources of MSCs that participate in bone formation [18].
In the intramembranous ossification, MSCs undergo proliferation and differentiation along the osteoblastic lineage to form bone directly without first forming cartilage. MSC and preosteoblast migration is involved in this process and are mediated by plentiful factors in vivo and in vitro. MSCs initially differentiate into preosteoblasts which proliferate near the bone surface and secrete ALP. Then they become mature osteoblasts and then form osteocytes which embedded in an extracellular matrix (ECM). Other factors also regulate the intramembranous ossification of MSCs such as Runx2, special AT-rich sequence binding protein 2 (SATB 2), and Osterix as well as pathways, like the wnt/β-catenin pathway and bone morphogenetic protein (BMP) pathway [17, 19].
In the endochondral ossification, MSCs are first condensed to initiate cartilage model formation. The process is mediated by BMPs through phosphorylating and activating receptor SMADs to transduce signals. During condensation, the central part of MSCs differentiates into chondrocytes and secretes cartilage matrix. While, other cells in the periphery, form the perichondrium that continues expressing type I collagen and other important factors, such as proteoglycans and ALP. Chondrocytes undergo rapid proliferation. Chondrocytes in the center become maturation, accompanied with an invasion of hypertrophic cartilage by the vasculature, followed by differentiation of osteoblasts within the perichondrium and marrow cavity. The inner perichondrium cells differentiate into osteoblasts, which secrete bone matrix to form the bone collar after vascularization in the hypertrophic cartilage. Many factors that regulate endochondral ossification are growth factors (GFs), transforming growth factor-β (TGF-β), Sry-related high-mobility group box 9 (Sox9) and Cell-to-cell interaction [17, 19].
Osteogenesis/ossification is the process in which new layers of bone tissue are placed by osteoblasts.
During bone formation, woven bone (haphazard arrangement of collagen fibers) is remodeled into lamellar bones (parallel bundles of collagen in a layer known as lamellae)
Periosteum is a connective tissue layer on the outer surface of the bone; the endosteum is a thin layer (generally only one layer of cell) that coats all the internal surfaces of the bone
Major cell of bone include: osteoblasts (from osteoprogenitor cells, forming osteoid that allow matrix mineralization to occur), osteocytes (from osteoblasts; closed to lacunae and retaining the matrix) and osteoclasts (from hemopoietic lineages; locally erodes matrix during bone formation and remodeling.
The process of bone formation occurs through two basic mechanisms:
Intramembranous bone formation occurs when bone forms inside the mesenchymal membrane. Bone tissue is directly laid on primitive connective tissue referred to mesenchyma without intermediate cartilage involvement. It forms bone of the skull and jaw; especially only occurs during development as well as the fracture repair.
Endochondral bone formation occurs when hyaline cartilage is used as a precursor to bone formation, then bone replaces hyaline cartilage, forms and grows all other bones, occurs during development and throughout life.
During interstitial epiphyseal growth (elongation of the bone), the growth plate with zonal organization of endochondral ossification, allows bone to lengthen without epiphyseal growth plates enlarging zones include:
Zone of resting.
Zone of proliferation.
Zone of hypertrophy.
Zone of calcification.
Zone of ossification and resorption.
During appositional growth, osteoclasts resorb old bone that lines the medullary cavity, while osteoblasts, via intramembranous ossification, produce new bone tissue beneath the periosteum.
Mesenchymal stem cell migration and differentiation are two important physiological processes in bone formation.
The author is grateful to Zahrona Kusuma Dewi for assistance with preparation of the manuscript.
The authors declare that there is no conflict of interests regarding the publication of this paper.
alkaline phosphatase biological apatite bone morphogenetic protein extracellular matrix growth factors mesenchymal stem cells runt-related transcription factor 2 special AT-rich sequence binding protein 2 sry-related high-mobility group box 9 transforming growth factor-β
Data mining is the process of analyzing large data stored in data warehouses in order to automatically extract hidden, previously unknown, valid, interesting, and actionable knowledge such as patterns, anomalies, associations, and changes. It has been commonly used in a wide range of different areas that include marketing, health care, military, environment, and education. Data mining is becoming increasingly important and essential for banking sector as well, since the amount of data collected by banks has grown remarkably and the need to discover hidden and useful patterns from banking data becomes widely recognized.
Banking systems collect huge amounts of data more rapidly as the number of channels (i.e., Internet banking, telebanking, retail banking, mobile banking, ATM) has increased. Banking data has been currently generated from various sources, including but not limited to bank account transactions, credit card details, loan applications, and telex messages. Hence, data mining can be used to extract meaningful information from these collected banking data, to enable banking institutions to make better decision-making process. For example, classification, which is one of the most popular data mining techniques, can be used to predict bank failures [1, 2, 3], to estimate bank customer churns [4], to detect frauds [5], and to evaluate loan approvals [6].
In many real-world banking applications, the distribution of the classes in the dataset is highly skewed. A bank data is imbalanced, when its target variable is categorical and if the number of samples in one class is significantly different from those of the other class(es). For example, in credit card fraud detection, most of the instances in the dataset are labeled as “non-fraud” (majority class), while very few are labeled as “fraud” (minority class). Similarly, in bank customer churn prediction, many instances are represented as negative class, whereas the minorities are marked as positive class. However, the performance of classification models is significantly affected by a skewed distribution of the classes; hence, this imbalance problem in the dataset may lead to bad estimates and misclassifications. Dealing with imbalanced data has been considered as one of the 10 most difficult problems in the field of data mining [7]. With this motivation, this paper proposes a class-based weighting strategy.
The main contribution of this paper is that it improves the decision jungle (DJ) method by a class-based weighting mechanism to make it effective in handling imbalanced data. In the proposed approach, a weight is assigned to each class based on its distribution, and this weight value is combined with class probabilities. The experimental studies conducted on 17 real-world banking datasets confirm that our approach generally performs better than the traditional decision jungle algorithm when the data is imbalanced.
The rest of this paper is organized as follows. Section 2 briefly presents the recent and related research in the literature. Section 3 describes the proposed approach, class-based weighted decision jungle method, in detail. Section 4 is devoted to the presentation and discussion of the experimental results, including the dataset descriptions. Finally, Section 5 gives the concluding remarks and provides some future research directions.
As a data-intensive sector, banking has been a popular application area for data mining researchers since the information technology revolution. The continuous developments in banking systems and the rapidly increasing availability of big banking data make data mining one of the most essential tasks for the banking industry.
Banking industries have used data mining techniques in various applications, especially on bank failure prediction [1, 2, 3], possible bank customer churns identification [4], fraudulent transaction detection [5], customer segmentation [8, 9, 10], predictions on bank telemarketing [11, 12, 13, 14], and sentiment analysis for bank customers [15]. Some of the classification studies in the banking sector have been compared in Table 1. The objectives of the studies, years they were conducted, algorithms and ensemble learning techniques they used, the country of the bank, and obtained results are shown in this table.
Ref | Year | Algorithms | Ensemble learning | Description | Country of the bank | Result | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
DT | NN | SVM | KNN | NB | LR | Bagging (i.e., RF) | Boosting (AB, XGB) | |||||
Manthoulis et al. [1] | 2020 | √ | √ | √ | Bank failure prediction | USA | AUC >0.97 | |||||
Ilham et al. [11] | 2019 | √ | √ | √ | √ | √ | √ | √ | Long-term deposit prediction | Portugal | ACC 97.07% | |
Lv et al. [5] | 2019 | √ | Fraud detection in bank accounts | — | ACC 97.39% | |||||||
Krishna et al. [15] | 2019 | √ | √ | √ | √ | √ | √ | √ | √ | Sentiment analysis for bank customers | India | AUC 0.8268 |
Farooqi and Iqbal [12] | 2019 | √ | √ | √ | √ | √ | Prediction of bank telemarketing outcomes | Portugal | ACC 91.2% | |||
Carmona et al. [2] | 2019 | √ | √ | √ | Bank failure prediction | USA | ACC 94.74% | |||||
Jing and Fang [3] | 2018 | √ | √ | √ | Bank failure prediction | USA | AUC 0.916 | |||||
Lahmiri [13] | 2017 | √ | Prediction of bank telemarketing outcomes | Portugal | ACC 71% | |||||||
Marinakos and Daskalaki [8] | 2017 | √ | √ | √ | √ | √ | Customer classification for bank direct marketing | Portugal | AUC 0.9 | |||
Keramati et al. [4] | 2016 | √ | Bank customer churn prediction | — | AUC 0.929 | |||||||
Wan et al. [6] | 2016 | √ | √ | √ | √ | √ | Predicting nonperforming loans | China | AUC 0.965 | |||
Ogwueleka et al. [10] | 2015 | √ | √ | Identifying bank customer behavior | Intercontinental | AUC 0.94 | ||||||
Moro et al. [14] | 2014 | √ | √ | √ | √ | Prediction of bank telemarketing outcomes | Portugal | AUC 0.8 | ||||
Smeureanu et al. [9] | 2013 | √ | √ | Customer segmentation in banking sector | Romania | ACC 97.127% |
Classification applications in the banking sector.
The main data mining tasks are classification (or categorical prediction), regression (or numeric prediction), clustering, association rule mining, and anomaly detection. Among these data mining tasks, classification is the most frequently used one in the banking sector [16], which is followed by clustering. Some banking applications [8, 10] have used more than one data mining techniques, among which clustering before classification has shown sufficient evidence of both popularity and applicability.
Apart from novel task-specific algorithms proposed by the authors, the most commonly used classification algorithms in the banking sector are decision tree (DT), neural network (NN), support vector machine (SVM), k-nearest neighbor (KNN), Naive Bayes (NB), and logistic regression (LR), as shown in Table 1. Some data mining studies in the banking sector [1, 2, 6, 11, 15] have used ensemble learning methods to increase the classification performance. Bagging and boosting are the most popular ensemble learning methods due to their theoretical performance advantages. Random forest (RF) [2, 6, 11, 15], AdaBoost (AB) [6], and extreme gradient boosting (XGB) [2, 15] have also been used in the banking sector as the most well-known bagging and boosting algorithms, respectively. As shown in Table 1, accuracy (ACC) and area under ROC curve (AUC) are the commonly used performance measures for classification.
Dealing with class imbalance problem, various solutions have been proposed in the literature. Such methods can be mainly grouped under two different approaches: (i) application of a data preprocessing step and (ii) modifying existing methods. The first approach focuses on balancing the dataset, which may be done either by increasing the number of minority class examples (over-sampling) or reducing the number of majority class examples (under-sampling). In the literature, synthetic minority over-sampling technique (SMOTE) [17] is commonly used as an over-sampling technique. As an alternative approach, some studies (i.e., [18]) focus on modifying the existing classification algorithms to make them more effective when dealing with imbalanced data. Unlike these studies, this paper proposes a novel approach (class-based weighting approach) to solve imbalanced data problem.
A decision jungle is an ensemble of rooted decision directed acyclic graphs (DAGs), which are powerful and compact distinct models for classification. While a traditional decision tree only allows one path to every node, a DAG in a DJ allows multiple paths from the root to each leaf [19]. During the training phase, node splitting and merging operations are done by the minimization of an objective function (the weighted sum of entropies at the leaves).
Unlike a decision forest that consists of several evolutionary induced decision trees, decision jungle consists of an ensemble of decision directed acyclic graphs. Experiments presented in [19] show that decision jungles require significantly less memory while significantly improving generalization, compared to decision forests and their variants.
In this study, we improve the decision jungle method by a class-based weighting mechanism to make it effective in dealing with imbalanced data.
Giving a training dataset D = {(x1, y1), (x2, y2), ..., (xn, yN)} that contains N instances, each instance is represented by a pair (x, y), where x is a d-dimensional vector such that xi = [xi1, xi2, ..., xid] and y is its corresponding class label. While x is defined as input variable, y is referred as output variable in the categorical domain Y = {y1, y2, ..., yk}, where k is the number of class labels. The goal is to learn a classifier function f: X → Y that optimizes some specific evaluation metric(s) and can predict the class label for unseen instances.
Training dataset is usually considered as a set of samples from a probability distribution F on X × Y. An instance component x is associated with a label class yj of Y such that:
where P(yj |x) is the predicted conditional probability of x belonging to yj and threshold is typically set to 1.
In this paper, we focus on imbalanced data problem, where the number of instances in one class (yi) is much larger or less than instances in the other class (yj). Like many other classification algorithms, the decision jungle method is also affected by a skewed distribution of the classes, because the traditional classifiers tend to be overwhelmed by the majority class and ignore the rare samples in the minority class. In order to overcome this problem, we locally adapted a class-based weighted mechanism, where weights are determined depending on the distribution of the class labels in the dataset. The main idea is that the minority class receives a higher weight, while the majority class is assigned with a lower weight during the combination class probabilities. According to this approach, the weight over a class is calculated as follows:
where Wc is the weight assigned to the class c, N is the total number of instances in the dataset, Nc is the number of instances present in the class c, and k is the number of class labels. In the proposed approach, Eq. (1) is updated as follows:
Figure 1 shows the general structure of the proposed approach. In the first step, various types of raw banking data are obtained from different sources such as account transactions, credit card details, loan applications, and social media texts. Next, raw banking data is preprocessed by applying several different techniques to provide data integration, data selection, and data transformation. The prepared data is then passed to the training step, where weighted decision jungle algorithm is used to build an effective model which accurately maps inputs to desired outputs. The classification validation step provides feedback to the learning phase for adjustment to improve model performance. The training phase is repeated until a desired classification performance is achieved. Once a model is build, after that it can be used to predict unseen data.
General structure of proposed approach.
We implemented the proposed approach in Azure Machine Learning Studio framework on cloud platform. In all experiments, default input parameters of the decision forest algorithm were used as follows:
Ensemble approach: Bagging
Number of decision DAGs: 8
Maximum width of the decision DAGs: 128
Maximum depth of the decision DAGs: 32
Number of optimization steps per decision DAG layer: 2048
Conventionally, accuracy is the most commonly used measure for evaluating a classifier performance. However, in the case of imbalanced data, accuracy is not sufficient alone since the minority class has very little impact on accuracy than the majority class. Using only accuracy measure is meaningless when the data is imbalanced and where the main learning target is the identification of the rare samples. In addition, accuracy does not distinguish between the numbers of correct class labels or misclassifications of different classes. Therefore, in this study, we also used several more metrics: macro-averaged precision, recall, and F-measure.
In this study, we conducted a series of experiments on 17 publically available real-world banking datasets which are described in Table 2. We obtained eight from the UCI Machine Learning Repository [20] and nine datasets from Kaggle data repository.
No | Dataset | #Instances | #Features | #Class | Majority class (%) | Minority class (%) | Data source | |
---|---|---|---|---|---|---|---|---|
1 | Abstract dataset for credit card fraud detection | 3075 | 12 | 2 | 85.4 | 14.6 | Kaggle | |
2 | Bank marketing [14] | Bank | 4521 | 17 | 2 | 88.5 | 11.5 | UCI |
3 | Bank full | 45,211 | 17 | 2 | 88.3 | 11.7 | UCI | |
4 | Bank additional | 4119 | 21 | 2 | 89.1 | 10.9 | UCI | |
5 | Bank additional full | 41,188 | 21 | 2 | 88.7 | 11.3 | UCI | |
6 | Bank customer churn prediction | 10,000 | 14 | 2 | 79.6 | 20.4 | Kaggle | |
7 | Bank loan status | 100,000 | 19 | 2 | 77.4 | 22.6 | Kaggle | |
8 | Banknote authentication | 1372 | 5 | 2 | 55.5 | 44.5 | UCI | |
9 | Credit approval | 690 | 16 | 2 | 55.5 | 44.5 | UCI | |
10 | Credit card fraud detection [21] | 284,807 | 31 | 2 | 99.8 | 0.2 | Kaggle | |
11 | Default of credit card clients [22] | 30,000 | 25 | 2 | 77.9 | 22.1 | UCI | |
12 | German credit | 1000 | 21 | 2 | 70.0 | 30.0 | UCI | |
13 | Give me some credit | 150,000 | 12 | 2 | 93.3 | 6.7 | Kaggle | |
14 | Loan campaign response | 20,000 | 40 | 2 | 87.4 | 12.6 | Kaggle | |
15 | Loan data for dummy bank | 887,379 | 30 | 2 | 92.4 | 7.6 | Kaggle | |
16 | Loan prediction | 614 | 13 | 2 | 68.7 | 31.3 | Kaggle | |
17 | Loan repayment prediction | 9578 | 14 | 2 | 84.0 | 16.0 | Kaggle |
The main characteristics of the banking datasets.
Table 3 shows the comparison of the classification performances of DJ and weighted DJ methods. According to the experimental results, on average, the weighted DJ method shows better classification outcome than its traditional version on the imbalanced banking datasets in terms of both accuracy and recall metrics. For example, the imbalanced dataset “bank additional” has an accuracy of 94.54% with the DJ method and 94.61% with the weighted DJ method. The accuracy is slightly higher with the weighted version because the classifier was able to classify the minority class samples better (0.8385, instead of 0.7914). The proposed method only disappointed in its accuracy and recall values for 4 of 17 datasets (with IDs 5, 9, 12, and 13).
ID | Dataset | Decision jungle | Class-based weighted decision jungle | ||||
---|---|---|---|---|---|---|---|
Acc (%) | Precision | Recall | Acc (%) | Precision | Recall | ||
1 | Abstract dataset for credit card fraud detection | 99.09 | 0.9918 | 0.9715 | 99.19 | 0.9923 | 0.9749 |
2 | Bank | 92.70 | 0.8909 | 0.7175 | 92.70 | 0.8492 | 0.7593 |
3 | Bank full | 91.06 | 0.8181 | 0.6874 | 91.17 | 0.8039 | 0.7217 |
4 | Bank additional | 94.54 | 0.9082 | 0.7914 | 94.61 | 0.8739 | 0.8385 |
5 | Bank additional full | 92.21 | 0.8332 | 0.7347 | 92.19 | 0.8126 | 0.7762 |
6 | Bank customer churn prediction | 87.37 | 0.8514 | 0.7291 | 87.40 | 0.8394 | 0.7411 |
7 | Bank loan status | 84.37 | 0.9170 | 0.6328 | 84.38 | 0.9169 | 0.6332 |
8 | Banknote authentication | 99.85 | 0.9987 | 0.9984 | 100.00 | 1.0000 | 1.0000 |
9 | Credit approval | 92.80 | 0.9273 | 0.9275 | 92.65 | 0.9257 | 0.9261 |
10 | Credit card fraud detection | 99.97 | 0.9915 | 0.9167 | 99.97 | 0.9861 | 0.9309 |
11 | Default of credit card clients | 83.05 | 0.7833 | 0.6695 | 83.16 | 0.7793 | 0.6785 |
12 | German credit | 86.30 | 0.8545 | 0.8088 | 85.70 | 0.8338 | 0.8198 |
13 | Give me some credit | 93.88 | 0.8245 | 0.5986 | 93.77 | 0.7861 | 0.6240 |
14 | Loan campaign response | 89.34 | 0.9393 | 0.5763 | 90.34 | 0.9390 | 0.6178 |
15 | Loan data for dummy bank | 95.19 | 0.9753 | 0.6837 | 95.20 | 0.9753 | 0.6844 |
16 | Loan prediction | 83.54 | 0.8715 | 0.7443 | 83.54 | 0.8631 | 0.7481 |
17 | Loan repayment prediction | 84.82 | 0.9059 | 0.5266 | 85.35 | 0.8900 | 0.5453 |
Average | 91.18 | 0.8990 | 0.7479 | 91.25 | 0.8863 | 0.7659 |
Comparison of unweighted and class-based weighted decision jungle methods in terms of accuracy, macro-averaged precision, and macro-averaged recall.
It is observed from the experiments that the weighted DJ method failed in classifying only one dataset among 17 datasets in terms of macro-averaged recall values. This means that the proposed method generally can be able to build a good model to predict minority class samples.
It can be deduced from the average precision and recall values that higher classification rates can be achieved with the weighted DJ method for minority classes, while more misclassified points in majority classes may also be detectable in the case of imbalanced data.
Figure 2 shows the comparison of the classification performances of two methods in terms of F-measure: decision jungle and class-based weighted decision jungle (weighted DJ). In principle, F-measure is defined as F = (2 × Recall × Precision)/(Recall + Precision), which is a harmonic mean between recall and precision. According to the results, for all banking datasets, the proposed method showed some increase or the same performance in the F-measure value.
Comparison of unweighted and class-based weighted decision jungle methods in terms of F-measure.
It can be possible to conclude from the experiments that the minority and majority ratios are not the only issues in constructing a good prediction model. For example, the minority and majority ratios of the first and last datasets are very close, but the classification outcomes related to these datasets are not similar. Although the minority and majority class ratios are almost the same for these two datasets, there is a significant difference between the classification accuracy, precision, and recall values of the datasets, as can be seen in Table 3. There is also a need for appropriate training examples that have data characteristics consistent with the class label assigned to them.
As a well-known data mining task, classification in real-world banking applications usually involves imbalanced datasets. In such cases, the performance of classification models is significantly affected by a skewed distribution of the classes. The data imbalance problem in the banking dataset may lead to bad estimates and misclassifications. To solve this problem, this paper proposes an approach which improves the decision jungle method with a class-based weighting mechanism. In the proposed approach, a weight is assigned to each class based on its distribution, and this weight value is combined with class probabilities. The empirical experiments conducted on 17 real-world bank datasets demonstrated that it is possible to improve the overall accuracy and recall values with the proposed approach.
As a future study, the proposed approach can be adapted for multi-label classification task. In addition, it can be enhanced for the ordinal classification problem.
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