{"id":"https://openalex.org/W1592274117","doi":"https://doi.org/10.1109/cec.2015.7257177","title":"Strategies for addressing class imbalance in ensemble classification of thermography breast cancer features","display_name":"Strategies for addressing class imbalance in ensemble classification of thermography breast cancer features","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1592274117","doi":"https://doi.org/10.1109/cec.2015.7257177","mag":"1592274117"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2015.7257177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2015.7257177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5105462955","display_name":"Gerald Schaefer","orcid":"https://orcid.org/0000-0003-1292-7674"},"institutions":[{"id":"https://openalex.org/I143804889","display_name":"Loughborough University","ror":"https://ror.org/04vg4w365","country_code":"GB","type":"education","lineage":["https://openalex.org/I143804889"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gerald Schaefer","raw_affiliation_strings":["Department of Computer Science, Loughborough University, Loughborough, U.K","[Department of Computer Science, Loughborough University, U.K.]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Loughborough University, Loughborough, U.K","institution_ids":["https://openalex.org/I143804889"]},{"raw_affiliation_string":"[Department of Computer Science, Loughborough University, U.K.]","institution_ids":["https://openalex.org/I143804889"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036331063","display_name":"Tomoharu Nakashima","orcid":"https://orcid.org/0000-0002-1443-0816"},"institutions":[{"id":"https://openalex.org/I15807432","display_name":"Osaka Prefecture University","ror":"https://ror.org/02cf1je33","country_code":"JP","type":"education","lineage":["https://openalex.org/I15807432"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoharu Nakashima","raw_affiliation_strings":["Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka, Japan","Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka, Japan","institution_ids":["https://openalex.org/I15807432"]},{"raw_affiliation_string":"Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Japan#TAB#","institution_ids":["https://openalex.org/I15807432"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2362","last_page":"2367"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9721999764442444,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9721999764442444,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.9226999878883362,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7427235841751099},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7256155610084534},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6978774070739746},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6437528133392334},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6195173263549805},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.614251434803009},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5663762092590332},{"id":"https://openalex.org/keywords/thermography","display_name":"Thermography","score":0.5412437915802002},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5274356007575989},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.5032965540885925},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46980008482933044},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.46237489581108093},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4486711323261261},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3521069288253784},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.3245370388031006},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.1431334912776947},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10677787661552429}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7427235841751099},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7256155610084534},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6978774070739746},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6437528133392334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6195173263549805},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.614251434803009},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5663762092590332},{"id":"https://openalex.org/C2779222261","wikidata":"https://www.wikidata.org/wiki/Q624587","display_name":"Thermography","level":3,"score":0.5412437915802002},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5274356007575989},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.5032965540885925},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46980008482933044},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.46237489581108093},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4486711323261261},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3521069288253784},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.3245370388031006},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.1431334912776947},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10677787661552429},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec.2015.7257177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2015.7257177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W53035228","https://openalex.org/W1527858862","https://openalex.org/W1542092351","https://openalex.org/W1563938718","https://openalex.org/W1589187426","https://openalex.org/W1594031697","https://openalex.org/W1970088130","https://openalex.org/W1977449438","https://openalex.org/W1979354414","https://openalex.org/W2006488700","https://openalex.org/W2009414485","https://openalex.org/W2015452969","https://openalex.org/W2016648380","https://openalex.org/W2018822866","https://openalex.org/W2023718290","https://openalex.org/W2030737769","https://openalex.org/W2059432853","https://openalex.org/W2065210076","https://openalex.org/W2104167780","https://openalex.org/W2113242816","https://openalex.org/W2124868070","https://openalex.org/W2132304941","https://openalex.org/W2133643452","https://openalex.org/W2136256517","https://openalex.org/W2139674793","https://openalex.org/W2148143831","https://openalex.org/W2148603752","https://openalex.org/W2156571267","https://openalex.org/W2157963336","https://openalex.org/W2158294624","https://openalex.org/W2167548992","https://openalex.org/W2330820318","https://openalex.org/W2487087946","https://openalex.org/W2911964244","https://openalex.org/W3085162807","https://openalex.org/W4232122439","https://openalex.org/W6633571703","https://openalex.org/W6682904970","https://openalex.org/W6684718744"],"related_works":["https://openalex.org/W1981866886","https://openalex.org/W2052615004","https://openalex.org/W2046975922","https://openalex.org/W4361733484","https://openalex.org/W4256395896","https://openalex.org/W2057416691","https://openalex.org/W1760344465","https://openalex.org/W2770076983","https://openalex.org/W4205397888","https://openalex.org/W4388700830"],"abstract_inverted_index":{"Thermography":[0],"provides":[1],"an":[2,98,156,164,175],"interesting":[3],"alternative":[4],"to":[5,23,31,93,97,110,140,215],"mammography":[6],"for":[7,76,89,129,134,220],"diagnosing":[8],"breast":[9],"cancer":[10],"as":[11,72],"it":[12,200],"is":[13,21,69,73,178,201],"a":[14,44,57,130,192],"noncontact,":[15],"non-invasive":[16],"and":[17,27,56,64,174,199,210],"passive":[18],"technique":[19],"that":[20,48,60,177,203],"able":[22],"detect":[24],"small":[25],"tumors":[26],"thus":[28],"can":[29],"lead":[30],"earlier":[32],"diagnosis.":[33],"Computer-aided":[34],"diagnostic":[35],"approaches":[36],"based":[37,179],"on":[38,171,180,191],"thermography":[39],"are":[40,83,169,189],"typically":[41],"split":[42],"into":[43],"feature":[45],"extraction":[46],"stage":[47,59,133],"derives":[49],"useful":[50],"information":[51],"from":[52],"the":[53,74,121,127,142,147,181],"thermogram":[54],"images,":[55],"classification":[58,100,132,208],"distinguishes":[61],"between":[62],"malignant":[63,94],"benign":[65,86],"cases.":[66],"The":[67],"latter":[68],"challenging":[70,193],"since,":[71],"case":[75],"many":[77],"medical":[78],"decision":[79,161],"making":[80],"problems,":[81],"there":[82],"(many)":[84],"more":[85],"cases":[87],"available":[88],"classifier":[90,218],"training":[91],"compared":[92,214],"cases,":[95],"leading":[96],"imbalanced":[99,221],"problem.":[101],"In":[102,152],"this":[103],"paper,":[104],"we":[105,136,154],"first":[106],"perform":[107,212],"image":[108],"analysis":[109],"identify":[111],"features":[112,124],"describing":[113],"bilateral":[114],"differences":[115],"in":[116,120,146],"regions":[117],"of":[118,149,159,183,195],"interest":[119],"thermogram.":[122],"These":[123],"then":[125],"form":[126],"input":[128],"pattern":[131],"which":[135],"present":[137],"several":[138],"strategies":[139,188],"address":[141],"existing":[143],"class":[144],"imbalance":[145],"context":[148],"ensemble":[150,157,165,176],"classifiers.":[151,185],"particular,":[153],"discuss":[155],"constructed":[158],"cost-sensitive":[160],"tree":[162],"classifiers,":[163],"whose":[166],"base":[167],"classifiers":[168],"trained":[170],"balanced":[172],"subspaces,":[173],"combination":[182],"one-class":[184],"All":[186],"three":[187],"evaluated":[190],"dataset":[194],"about":[196],"150":[197],"thermograms":[198],"shown":[202],"they":[204],"provide":[205],"very":[206],"good":[207],"performance":[209],"furthermore":[211],"favourably":[213],"other":[216],"state-of-the-art":[217],"ensembles":[219],"data.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
