{"id":"https://openalex.org/W4385566581","doi":"https://doi.org/10.1016/j.knosys.2023.110831","title":"An AUC-maximizing classifier for skewed and partially labeled data with an application in clinical prediction modeling","display_name":"An AUC-maximizing classifier for skewed and partially labeled data with an application in clinical prediction modeling","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385566581","doi":"https://doi.org/10.1016/j.knosys.2023.110831"},"language":"en","primary_location":{"id":"doi:10.1016/j.knosys.2023.110831","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.knosys.2023.110831","pdf_url":null,"source":{"id":"https://openalex.org/S10169007","display_name":"Knowledge-Based Systems","issn_l":"0950-7051","issn":["0950-7051","1872-7409"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge-Based Systems","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.knosys.2023.110831","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051413585","display_name":"Guanjin Wang","orcid":"https://orcid.org/0000-0002-5258-0532"},"institutions":[{"id":"https://openalex.org/I176790772","display_name":"Murdoch University","ror":"https://ror.org/00r4sry34","country_code":"AU","type":"education","lineage":["https://openalex.org/I176790772"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Guanjin Wang","raw_affiliation_strings":["School of Information Technology, Murdoch University, Murdoch, WA, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Murdoch University, Murdoch, WA, Australia","institution_ids":["https://openalex.org/I176790772"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112873991","display_name":"Stephen Wai Hang Kwok","orcid":null},"institutions":[{"id":"https://openalex.org/I176790772","display_name":"Murdoch University","ror":"https://ror.org/00r4sry34","country_code":"AU","type":"education","lineage":["https://openalex.org/I176790772"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Stephen Wai Hang Kwok","raw_affiliation_strings":["Harry Butler Institute, Murdoch University, WA, Australia"],"affiliations":[{"raw_affiliation_string":"Harry Butler Institute, Murdoch University, WA, Australia","institution_ids":["https://openalex.org/I176790772"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092177938","display_name":"Daniel Axford","orcid":null},"institutions":[{"id":"https://openalex.org/I176790772","display_name":"Murdoch University","ror":"https://ror.org/00r4sry34","country_code":"AU","type":"education","lineage":["https://openalex.org/I176790772"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Daniel Axford","raw_affiliation_strings":["School of Information Technology, Murdoch University, Murdoch, WA, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Murdoch University, Murdoch, WA, Australia","institution_ids":["https://openalex.org/I176790772"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074121150","display_name":"Mohammed Yousufuddin","orcid":"https://orcid.org/0000-0002-7496-9856"},"institutions":[{"id":"https://openalex.org/I4210123409","display_name":"Mayo Clinic Health System","ror":"https://ror.org/02zzw8g45","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210123409"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammed Yousufuddin","raw_affiliation_strings":["Department of Internal Medicine, Mayo Clinic Health System, Austin, MN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Internal Medicine, Mayo Clinic Health System, Austin, MN, USA","institution_ids":["https://openalex.org/I4210123409"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002219416","display_name":"Ferdous Sohel","orcid":"https://orcid.org/0000-0003-1557-4907"},"institutions":[{"id":"https://openalex.org/I176790772","display_name":"Murdoch University","ror":"https://ror.org/00r4sry34","country_code":"AU","type":"education","lineage":["https://openalex.org/I176790772"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ferdous Sohel","raw_affiliation_strings":["School of Information Technology, Murdoch University, Murdoch, WA, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Murdoch University, Murdoch, WA, Australia","institution_ids":["https://openalex.org/I176790772"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5051413585"],"corresponding_institution_ids":["https://openalex.org/I176790772"],"apc_list":{"value":3130,"currency":"USD","value_usd":3130},"apc_paid":{"value":3130,"currency":"USD","value_usd":3130},"fwci":0.3497,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64554374,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"278","issue":null,"first_page":"110831","last_page":"110831"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9976999759674072,"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":0.9976999759674072,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9970999956130981,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9958999752998352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6711557507514954},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6402561664581299},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.620501697063446},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5957070589065552},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5229309797286987},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.48414191603660583},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.47896841168403625},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4476649761199951},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4236098527908325},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34224236011505127},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1649882197380066},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08414968848228455}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6711557507514954},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6402561664581299},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.620501697063446},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5957070589065552},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5229309797286987},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.48414191603660583},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.47896841168403625},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4476649761199951},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4236098527908325},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34224236011505127},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1649882197380066},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08414968848228455},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.knosys.2023.110831","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.knosys.2023.110831","pdf_url":null,"source":{"id":"https://openalex.org/S10169007","display_name":"Knowledge-Based Systems","issn_l":"0950-7051","issn":["0950-7051","1872-7409"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge-Based Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.knosys.2023.110831","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.knosys.2023.110831","pdf_url":null,"source":{"id":"https://openalex.org/S10169007","display_name":"Knowledge-Based Systems","issn_l":"0950-7051","issn":["0950-7051","1872-7409"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge-Based Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1596717185","https://openalex.org/W2020089616","https://openalex.org/W2035149165","https://openalex.org/W2063944225","https://openalex.org/W2069231281","https://openalex.org/W2081215732","https://openalex.org/W2106401878","https://openalex.org/W2107968230","https://openalex.org/W2148143831","https://openalex.org/W2149298154","https://openalex.org/W2154134600","https://openalex.org/W2157825442","https://openalex.org/W2164071167","https://openalex.org/W2396349253","https://openalex.org/W2404420681","https://openalex.org/W2550999023","https://openalex.org/W2763619424","https://openalex.org/W2884214082","https://openalex.org/W2900266038","https://openalex.org/W2947641277","https://openalex.org/W2964050365","https://openalex.org/W2984353870","https://openalex.org/W2997706891","https://openalex.org/W3006829358","https://openalex.org/W3011573100","https://openalex.org/W3011867254","https://openalex.org/W3015997807","https://openalex.org/W3023635941","https://openalex.org/W3024473016","https://openalex.org/W3084258740","https://openalex.org/W3087004455","https://openalex.org/W3149348938","https://openalex.org/W3183862858","https://openalex.org/W3195599616","https://openalex.org/W3200830065","https://openalex.org/W4205139417","https://openalex.org/W4206087710","https://openalex.org/W4224324554","https://openalex.org/W4242866185","https://openalex.org/W4243367342","https://openalex.org/W4281717669","https://openalex.org/W4289619803","https://openalex.org/W4292084517","https://openalex.org/W4292974997","https://openalex.org/W4311543568","https://openalex.org/W4324054968","https://openalex.org/W4376643503","https://openalex.org/W6652192942","https://openalex.org/W6656269930","https://openalex.org/W6676348322","https://openalex.org/W6713546675","https://openalex.org/W6740887809","https://openalex.org/W6746539935","https://openalex.org/W6754106242","https://openalex.org/W6768802926","https://openalex.org/W6775160663","https://openalex.org/W6798500288","https://openalex.org/W6811159002","https://openalex.org/W6852702725"],"related_works":["https://openalex.org/W4361804730","https://openalex.org/W2142113611","https://openalex.org/W2334467465","https://openalex.org/W2018387840","https://openalex.org/W2087870008","https://openalex.org/W2045629210","https://openalex.org/W2162534555","https://openalex.org/W2752178021","https://openalex.org/W2143024819","https://openalex.org/W2964201926"],"abstract_inverted_index":{"Partially":[0],"labeled":[1,138],"and":[2,17,23,78,99,139,190],"skewed":[3,70,140],"datasets":[4,71,189],"are":[5],"common":[6],"in":[7,106,172,221],"many":[8],"applications":[9],"including":[10],"healthcare,":[11],"due":[12],"to":[13,46,129,211],"the":[14,53,56,118,121,131,136,145,158,199,204],"high":[15],"costs":[16],"time":[18],"constraints":[19],"of":[20,120,178],"data":[21,32,196],"collection":[22],"annotation.":[24],"However,":[25],"training":[26],"machine":[27],"learning":[28,108,174],"classifiers":[29],"on":[30,52,93,135,186],"such":[31,75,167],"can":[33,126],"undermine":[34],"their":[35],"prediction":[36,215],"performances.":[37],"In":[38,203],"this":[39,48],"paper,":[40],"we":[41,208],"propose":[42],"a":[43,64,83,102,191,213],"novel":[44],"classifier":[45,85],"address":[47],"problem":[49],"by":[50,115],"focusing":[51],"Area":[54],"Under":[55],"Curve":[57],"(AUC),":[58],"which":[59,90,225],"is":[60,91],"widely":[61],"recognized":[62],"as":[63,76,161,168],"more":[65],"robust":[66],"performance":[67],"metric":[68,105],"for":[69,217],"than":[72],"other":[73],"metrics":[74],"accuracy":[77],"error":[79],"rate.":[80],"We":[81],"introduce":[82],"new":[84,103],"called":[86],"PSVM-AUC":[87],"Maximizer":[88],"(PSVM-AUCMax)":[89],"based":[92],"Proximal":[94],"Support":[95],"Vector":[96],"Machines":[97],"(PSVM)":[98],"directly":[100,116],"maximizes":[101],"AUC-based":[104,123],"its":[107,165],"objective.":[109],"PSVM-AUCMax":[110,125,179,210],"has":[111,180],"several":[112,187],"merits.":[113],"First,":[114],"integrating":[117],"maximization":[119],"proposed":[122],"metric,":[124],"be":[127],"proved":[128],"have":[130],"enhanced":[132],"generalization":[133],"capability":[134],"partially":[137],"dataset.":[141],"Second,":[142],"it":[143],"simplifies":[144],"model":[146,216],"selection":[147],"process":[148],"with":[149],"fewer":[150],"tuning":[151],"hyperparameters.":[152],"Third,":[153],"PSVM-AUCMax\u2019s":[154],"analytical":[155],"solution":[156],"remains":[157],"same":[159],"form":[160],"traditional":[162],"PSVM,":[163],"preserving":[164],"advantages":[166],"fast":[169],"incremental":[170,173],"updating":[171],"scenarios.":[175],"The":[176],"efficacy":[177],"been":[181],"demonstrated":[182],"through":[183],"extensive":[184],"experiments":[185],"public":[188],"healthcare":[192,205],"case":[193,206],"study":[194],"using":[195],"collected":[197],"at":[198],"US":[200],"Mayo":[201],"Clinic.":[202],"study,":[207],"utilized":[209],"develop":[212],"clinical":[214],"forecasting":[218],"composite":[219],"outcomes":[220],"hospitalized":[222],"COVID-19":[223],"patients":[224],"yielded":[226],"promising":[227],"results.":[228]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
