{"id":"https://openalex.org/W4417147622","doi":"https://doi.org/10.1016/j.neucom.2025.132361","title":"Training neural network classifiers on multi-class imbalanced data via competitive coevolution","display_name":"Training neural network classifiers on multi-class imbalanced data via competitive coevolution","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W4417147622","doi":"https://doi.org/10.1016/j.neucom.2025.132361"},"language":"en","primary_location":{"id":"doi:10.1016/j.neucom.2025.132361","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2025.132361","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.neucom.2025.132361","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079759495","display_name":"Marco Castellani","orcid":"https://orcid.org/0000-0002-5623-7491"},"institutions":[{"id":"https://openalex.org/I1330855593","display_name":"Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology","ror":"https://ror.org/05bc5bx80","country_code":"IN","type":"education","lineage":["https://openalex.org/I1330855593"]},{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB","IN"],"is_corresponding":true,"raw_author_name":"Marco Castellani","raw_affiliation_strings":["Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom","Vel Tech Institute of Science and Technology, 400 feet Outer Ring Road, Avadi, Chennai, 600 062, India"],"raw_orcid":"https://orcid.org/0000-0002-5623-7491","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom","institution_ids":["https://openalex.org/I79619799"]},{"raw_affiliation_string":"Vel Tech Institute of Science and Technology, 400 feet Outer Ring Road, Avadi, Chennai, 600 062, India","institution_ids":["https://openalex.org/I1330855593"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5079759495"],"corresponding_institution_ids":["https://openalex.org/I1330855593","https://openalex.org/I79619799"],"apc_list":{"value":2470,"currency":"USD","value_usd":2470},"apc_paid":{"value":2470,"currency":"USD","value_usd":2470},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20011118,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"667","issue":null,"first_page":"132361","last_page":"132361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.7698000073432922,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.7698000073432922,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.03060000017285347,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.020899999886751175,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.9488000273704529},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5875999927520752},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5472999811172485},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.5098999738693237},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46399998664855957},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4401000142097473},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.43700000643730164},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.400299996137619},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.38519999384880066}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.9488000273704529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7269999980926514},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7052000164985657},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6413999795913696},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5875999927520752},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.5098999738693237},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.43700000643730164},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.400299996137619},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.3792000114917755},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3531000018119812},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34459999203681946},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.2809999883174896},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.2702000141143799},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.neucom.2025.132361","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2025.132361","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:openaire/55aec54c-1e69-45b3-b4f3-9d8b74e41d9b","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/55aec54c-1e69-45b3-b4f3-9d8b74e41d9b","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Castellani, M 2025, 'Training neural network classifiers on multi-class imbalanced data via competitive coevolution', Neurocomputing. https://doi.org/10.1016/j.neucom.2025.132361","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1016/j.neucom.2025.132361","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2025.132361","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1754612754","https://openalex.org/W1941659294","https://openalex.org/W1974079881","https://openalex.org/W1984836098","https://openalex.org/W1993220166","https://openalex.org/W2011376672","https://openalex.org/W2012027153","https://openalex.org/W2015452969","https://openalex.org/W2018186689","https://openalex.org/W2023448113","https://openalex.org/W2032867948","https://openalex.org/W2037592308","https://openalex.org/W2072245049","https://openalex.org/W2083551746","https://openalex.org/W2087787741","https://openalex.org/W2099454382","https://openalex.org/W2107686700","https://openalex.org/W2118978333","https://openalex.org/W2119191234","https://openalex.org/W2119498311","https://openalex.org/W2122111042","https://openalex.org/W2124710650","https://openalex.org/W2128965734","https://openalex.org/W2137983211","https://openalex.org/W2148143831","https://openalex.org/W2148524673","https://openalex.org/W2164341120","https://openalex.org/W2164921999","https://openalex.org/W2185967890","https://openalex.org/W2338318698","https://openalex.org/W2604248332","https://openalex.org/W2736435690","https://openalex.org/W2912934387","https://openalex.org/W2964050365","https://openalex.org/W2966800547","https://openalex.org/W2990580840","https://openalex.org/W3014150937","https://openalex.org/W3014666486","https://openalex.org/W3082059448","https://openalex.org/W3120644841","https://openalex.org/W3127368173","https://openalex.org/W4206330621","https://openalex.org/W4210434492","https://openalex.org/W4246795719","https://openalex.org/W4281717669","https://openalex.org/W4296640253","https://openalex.org/W4311257868","https://openalex.org/W4312204367","https://openalex.org/W4318197049","https://openalex.org/W4321460235","https://openalex.org/W4385296331","https://openalex.org/W4385789921","https://openalex.org/W4386253308","https://openalex.org/W4386632303","https://openalex.org/W4391688210","https://openalex.org/W4392001422","https://openalex.org/W4396220530","https://openalex.org/W4396669359","https://openalex.org/W4396773505","https://openalex.org/W4412867641"],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,28,32,95,103,109,124,128,138,146,153,160,164,169,173,185,189,198],"proposed":[2,129,154,199],"coevolutionary":[3,77],"scheme,":[4],"a":[5,14,84,98,202],"population":[6,15,85],"of":[7,16,48,71,73,86,97,127,152,188,207,280],"classifiers":[8,22],"(the":[9,19],"predators)":[10],"is":[11,201],"pitted":[12],"against":[13],"training":[17,29,33,83,192,243],"patterns":[18,34],"prey).":[20],"The":[21,121,149,194],"learn":[23],"to":[24,43,60,94,132,233],"correctly":[25],"identify":[26],"(capture)":[27],"patterns,":[30],"while":[31,176],"are":[35],"selected":[36],"based":[37],"on":[38,54,108,137,145,163,172,247],"their":[39],"misclassification":[40],"rate":[41],"(ability":[42],"avoid":[44],"capture).":[45],"This":[46],"mechanism":[47],"intelligent":[49],"data":[50,117,180,213,216],"undersampling":[51,118],"was":[52,92,231,275],"tested":[53,246],"twelve":[55],"multi-class":[56],"learning":[57,170,237,258,267],"problems,":[58],"corresponding":[59],"four":[61],"different":[62,69],"feature":[63],"spaces":[64],"each":[65,106],"sampled":[66],"with":[67,82],"three":[68],"levels":[70],"severity":[72],"class":[74,140,236],"imbalance.":[75],"Two":[76,240],"algorithms":[78,244],"were":[79,156,245],"evaluated,":[80],"tasked":[81],"multi-layer":[87],"perceptron":[88],"classifiers.":[89],"Their":[90],"performance":[91,96],"compared":[93],"standard":[99,255],"evolutionary":[100,256],"algorithm":[101],"and":[102,141,220,223,259,270],"backpropagation":[104],"rule,":[105],"used":[107],"raw":[110],"imbalanced":[111,235],"data,":[112],"or":[113,119],"distributions":[114],"rebalanced":[115],"via":[116],"oversampling.":[120],"tests":[122,195],"highlighted":[123],"superior":[125],"accuracy":[126,162],"approach,":[130],"thanks":[131],"its":[133],"competitive":[134],"recognition":[135,143],"rates":[136,144],"minority":[139],"top":[142],"other":[147],"classes.":[148],"computational":[150,186,281],"overheads":[151],"approach":[155,200],"modest.":[157],"Undersampling":[158],"boosted":[159],"classification":[161,209],"underrepresented":[165],"classes,":[166,175],"but":[167],"degraded":[168],"results":[171],"remaining":[174],"oversampling":[177],"created":[178],"large":[179,212],"sets":[181],"that":[182,197],"considerably":[183],"increased":[184],"cost":[187],"neural":[190,225],"network":[191,226],"procedure.":[193],"indicated":[196],"promising":[203],"solution":[204],"for":[205],"many":[206],"today\u2019s":[208],"problems":[210],"involving":[211],"sets,":[214],"complex":[215],"structures":[217],"like":[218],"images":[219],"point":[221],"clouds,":[222],"deep":[224],"architectures.":[227],"\u2022":[228,239,251,261,272],"Competitive":[229,252,262,273],"coevolution":[230,253,263,274],"demonstrated":[232],"solve":[234],"problems.":[238],"predator\u2013prey":[241],"ANN":[242,257],"12":[248],"purpose-built":[249],"benchmarks.":[250],"outperformed":[254],"BP.":[260],"enabled":[264],"more":[265],"accurate":[266],"than":[268],"under-sampling":[269],"over-sampling.":[271],"very":[276],"economical":[277],"in":[278],"terms":[279],"overheads.":[282]},"counts_by_year":[],"updated_date":"2026-02-22T13:39:03.778224","created_date":"2025-12-09T00:00:00"}
