{"id":"https://openalex.org/W3038266714","doi":"https://doi.org/10.1109/tfuzz.2020.3026144","title":"Machine Learning With the Sugeno Integral: The Case of Binary Classification","display_name":"Machine Learning With the Sugeno Integral: The Case of Binary Classification","publication_year":2020,"publication_date":"2020-09-23","ids":{"openalex":"https://openalex.org/W3038266714","doi":"https://doi.org/10.1109/tfuzz.2020.3026144","mag":"3038266714"},"language":"en","primary_location":{"id":"doi:10.1109/tfuzz.2020.3026144","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tfuzz.2020.3026144","pdf_url":null,"source":{"id":"https://openalex.org/S134177497","display_name":"IEEE Transactions on Fuzzy Systems","issn_l":"1063-6706","issn":["1063-6706","1941-0034"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Fuzzy Systems","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.03046","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021415858","display_name":"Sadegh Abbaszadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I206945453","display_name":"Paderborn University","ror":"https://ror.org/058kzsd48","country_code":"DE","type":"education","lineage":["https://openalex.org/I206945453"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sadegh Abbaszadeh","raw_affiliation_strings":["Department of Computer Science, Heinz Nixdorf Institute, Paderborn University, Paderborn, Germany","Paderborn Univ"],"raw_orcid":"https://orcid.org/0000-0002-8257-0116","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Heinz Nixdorf Institute, Paderborn University, Paderborn, Germany","institution_ids":["https://openalex.org/I206945453"]},{"raw_affiliation_string":"Paderborn Univ","institution_ids":["https://openalex.org/I206945453"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059439673","display_name":"Eyke H\u00fcllermeier","orcid":"https://orcid.org/0000-0002-9944-4108"},"institutions":[{"id":"https://openalex.org/I206945453","display_name":"Paderborn University","ror":"https://ror.org/058kzsd48","country_code":"DE","type":"education","lineage":["https://openalex.org/I206945453"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Eyke Hullermeier","raw_affiliation_strings":["Department of Computer Science, Heinz Nixdorf Institute, Paderborn University, Paderborn, Germany","Paderborn Univ"],"raw_orcid":"https://orcid.org/0000-0002-9944-4108","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Heinz Nixdorf Institute, Paderborn University, Paderborn, Germany","institution_ids":["https://openalex.org/I206945453"]},{"raw_affiliation_string":"Paderborn Univ","institution_ids":["https://openalex.org/I206945453"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I206945453"],"apc_list":null,"apc_paid":null,"fwci":0.5361,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.74008673,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"29","issue":"12","first_page":"3723","last_page":"3733"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9991999864578247,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9991999864578247,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9984999895095825,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.994700014591217,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7156976461410522},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.6483312845230103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6471362113952637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6243395805358887},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6237598657608032},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4894186556339264},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41878047585487366},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4169689118862152},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19362610578536987},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10519078373908997}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7156976461410522},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.6483312845230103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6471362113952637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6243395805358887},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6237598657608032},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4894186556339264},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41878047585487366},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4169689118862152},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19362610578536987},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10519078373908997},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tfuzz.2020.3026144","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tfuzz.2020.3026144","pdf_url":null,"source":{"id":"https://openalex.org/S134177497","display_name":"IEEE Transactions on Fuzzy Systems","issn_l":"1063-6706","issn":["1063-6706","1941-0034"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Fuzzy Systems","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2007.03046","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.03046","pdf_url":"https://arxiv.org/pdf/2007.03046","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3038266714","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2007.03046.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2007.03046","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2007.03046","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.03046","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.03046","pdf_url":"https://arxiv.org/pdf/2007.03046","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.4399999976158142,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1197622812","https://openalex.org/W1530525332","https://openalex.org/W1560724230","https://openalex.org/W1592572926","https://openalex.org/W1846716565","https://openalex.org/W1869882793","https://openalex.org/W1919232813","https://openalex.org/W1969855798","https://openalex.org/W1970041071","https://openalex.org/W1987205551","https://openalex.org/W2003874621","https://openalex.org/W2038105248","https://openalex.org/W2049466465","https://openalex.org/W2061760968","https://openalex.org/W2084160675","https://openalex.org/W2087567316","https://openalex.org/W2090961061","https://openalex.org/W2104140572","https://openalex.org/W2106689827","https://openalex.org/W2108927591","https://openalex.org/W2109603199","https://openalex.org/W2124466170","https://openalex.org/W2133990480","https://openalex.org/W2138907258","https://openalex.org/W2144364256","https://openalex.org/W2148603752","https://openalex.org/W2211238471","https://openalex.org/W2625719506","https://openalex.org/W2741686183","https://openalex.org/W2768983211","https://openalex.org/W2781649875","https://openalex.org/W2898153791","https://openalex.org/W2912792553","https://openalex.org/W2914354338","https://openalex.org/W2948867214","https://openalex.org/W2980215768","https://openalex.org/W2981015291","https://openalex.org/W2996949616","https://openalex.org/W3008787144","https://openalex.org/W3034992991","https://openalex.org/W3043771091","https://openalex.org/W4232799716","https://openalex.org/W4252793191","https://openalex.org/W6676165111","https://openalex.org/W6676504727","https://openalex.org/W6763722398","https://openalex.org/W6770955917"],"related_works":["https://openalex.org/W3088246026","https://openalex.org/W2019517366","https://openalex.org/W3132884395","https://openalex.org/W2402220101","https://openalex.org/W2305870132","https://openalex.org/W2460874728","https://openalex.org/W2765611817","https://openalex.org/W3034148736","https://openalex.org/W2778475858","https://openalex.org/W3109542001","https://openalex.org/W2984692558","https://openalex.org/W1758923908","https://openalex.org/W2774808155","https://openalex.org/W2901953814","https://openalex.org/W169454511","https://openalex.org/W2273828870","https://openalex.org/W2292290481","https://openalex.org/W2768783441","https://openalex.org/W2746002367","https://openalex.org/W1834371390"],"abstract_inverted_index":{"In":[0],"this":[1,66,117],"article,":[2],"we":[3,20,119,175,196],"elaborate":[4],"on":[5,124,155,203],"the":[6,9,13,29,59,63,102,109,112,136,156,161,164,168,172,182,188],"use":[7],"of":[8,15,43,62,101,107,163,170,184,187],"Sugeno":[10,30,64,113],"integral":[11,31],"in":[12,27,94,194],"context":[14],"machine":[16,95],"learning.":[17],"More":[18],"specifically,":[19],"propose":[21],"a":[22,53,86,131,149,153,185],"method":[23,150,199],"for":[24,71,134,151],"binary":[25],"classification,":[26],"which":[28,195],"is":[32,68,85],"used":[33],"as":[34,146,148],"an":[35,44,121],"aggregation":[36],"function":[37],"that":[38,88],"combines":[39],"several":[40,204],"local":[41,141],"evaluations":[42,142],"instance,":[45],"pertaining":[46],"to":[47,58],"different":[48],"features,":[49],"or":[50],"measurements,":[51],"into":[52,140],"single":[54],"global":[55,157],"evaluation.":[56,158],"Due":[57],"specific":[60],"nature":[61],"integral,":[65],"approach":[67,178],"especially":[69],"suitable":[70,132],"learning":[72,96,103],"from":[73,81],"ordinal":[74,82],"data,":[75,174],"i.e.,":[76],"when":[77],"measurements":[78],"are":[79],"taken":[80],"scales.":[83],"This":[84],"topic":[87],"has":[89],"not":[90],"received":[91],"much":[92],"attention":[93],"so":[97],"far.":[98],"The":[99,127],"core":[100],"problem":[104,169],"itself":[105],"consists":[106],"identifying":[108],"capacity":[110],"underlying":[111],"integral.":[114],"To":[115,159],"tackle":[116],"problem,":[118],"develop":[120],"algorithm":[122,128],"based":[123],"linear":[125],"programming.":[126],"also":[129],"includes":[130],"technique":[133],"transforming":[135],"original":[137],"feature":[138],"values":[139],"(local":[143],"utility":[144],"scores),":[145],"well":[147],"tuning":[152],"threshold":[154],"control":[160],"flexibility":[162],"classifier":[165],"and":[166],"mitigate":[167],"overfitting":[171],"training":[173],"generalize":[176],"our":[177,198],"toward$k$-maxitive":[179],"capacities,":[180],"where$k$plays":[181],"role":[183],"hyperparameter":[186],"learner.":[189],"We":[190],"present":[191],"experimental":[192],"studies,":[193],"compare":[197],"with":[200],"competing":[201],"approaches":[202],"benchmark":[205],"datasets.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}
