{"id":"https://openalex.org/W2953926703","doi":"https://doi.org/10.1109/fuzz-ieee.2019.8858821","title":"Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures","display_name":"Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2953926703","doi":"https://doi.org/10.1109/fuzz-ieee.2019.8858821","mag":"2953926703"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz-ieee.2019.8858821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2019.8858821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://nottingham-repository.worktribe.com/output/2635158","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080627528","display_name":"Utkarsh Agrawal","orcid":"https://orcid.org/0000-0001-5181-6120"},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Utkarsh Agrawal","raw_affiliation_strings":["Intelligent Modelling and Analysis (IMA) Group and Lab for Uncertainty in Data and Decision Making (LUCID), School of Computer Science, University of Nottingham, Nottingham, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Modelling and Analysis (IMA) Group and Lab for Uncertainty in Data and Decision Making (LUCID), School of Computer Science, University of Nottingham, Nottingham, UK","institution_ids":["https://openalex.org/I142263535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070095814","display_name":"Christian Wagner","orcid":"https://orcid.org/0000-0002-6121-9722"},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Christian Wagner","raw_affiliation_strings":["Intelligent Modelling and Analysis (IMA) Group and Lab for Uncertainty in Data and Decision Making (LUCID), School of Computer Science, University of Nottingham, Nottingham, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Modelling and Analysis (IMA) Group and Lab for Uncertainty in Data and Decision Making (LUCID), School of Computer Science, University of Nottingham, Nottingham, UK","institution_ids":["https://openalex.org/I142263535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040516036","display_name":"Jonathan M. Garibaldi","orcid":"https://orcid.org/0000-0002-9690-7074"},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jonathan M. Garibaldi","raw_affiliation_strings":["Intelligent Modelling and Analysis (IMA) Group and Lab for Uncertainty in Data and Decision Making (LUCID), School of Computer Science, University of Nottingham, Nottingham, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Modelling and Analysis (IMA) Group and Lab for Uncertainty in Data and Decision Making (LUCID), School of Computer Science, University of Nottingham, Nottingham, UK","institution_ids":["https://openalex.org/I142263535"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006072420","display_name":"Daniele Soria","orcid":"https://orcid.org/0000-0002-0164-8218"},"institutions":[{"id":"https://openalex.org/I94951947","display_name":"University of Westminster","ror":"https://ror.org/04ycpbx82","country_code":"GB","type":"education","lineage":["https://openalex.org/I94951947"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Daniele Soria","raw_affiliation_strings":["School of Computer Science and Engineering, University of Westminster, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Westminster, London, UK","institution_ids":["https://openalex.org/I94951947"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10050","display_name":"Multi-Criteria Decision Making","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10050","display_name":"Multi-Criteria Decision Making","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9847999811172485,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9693999886512756,"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/choquet-integral","display_name":"Choquet integral","score":0.6849929094314575},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.6673591732978821},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6535370945930481},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.6482969522476196},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.6407045722007751},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6404259204864502},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6172745227813721},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5665014982223511},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5285022854804993},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.47130095958709717},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4488426446914673},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42989256978034973},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.4293709695339203},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.23220503330230713}],"concepts":[{"id":"https://openalex.org/C112799922","wikidata":"https://www.wikidata.org/wiki/Q5104911","display_name":"Choquet integral","level":3,"score":0.6849929094314575},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.6673591732978821},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6535370945930481},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.6482969522476196},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.6407045722007751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6404259204864502},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6172745227813721},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5665014982223511},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5285022854804993},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.47130095958709717},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4488426446914673},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42989256978034973},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.4293709695339203},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.23220503330230713},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/fuzz-ieee.2019.8858821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2019.8858821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},{"id":"pmh:oai:kar.kent.ac.uk:98866","is_oa":false,"landing_page_url":"https://doi.org/10.1109/FUZZ-IEEE.2019.8858821>)","pdf_url":null,"source":{"id":"https://openalex.org/S4377196264","display_name":"Kent Academic Repository (University of Kent)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20581793","host_organization_name":"University of Kent","host_organization_lineage":["https://openalex.org/I20581793"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Conference proceeding"},{"id":"pmh:oai:westminsterresearch.westminster.ac.uk:qv274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/FUZZ-IEEE.2019.8858821","pdf_url":null,"source":{"id":"https://openalex.org/S4306400277","display_name":"WestminsterResearch (University of Westminster)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I94951947","host_organization_name":"University of Westminster","host_organization_lineage":["https://openalex.org/I94951947"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"conference-paper"},{"id":"pmh:oai:nottingham-repository.worktribe.com:2635158","is_oa":true,"landing_page_url":"https://nottingham-repository.worktribe.com/output/2635158","pdf_url":null,"source":{"id":"https://openalex.org/S4306402483","display_name":"Repository@Nottingham (University of Nottingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I142263535","host_organization_name":"University of Nottingham","host_organization_lineage":["https://openalex.org/I142263535"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Presentation / Conference Contribution"}],"best_oa_location":{"id":"pmh:oai:nottingham-repository.worktribe.com:2635158","is_oa":true,"landing_page_url":"https://nottingham-repository.worktribe.com/output/2635158","pdf_url":null,"source":{"id":"https://openalex.org/S4306402483","display_name":"Repository@Nottingham (University of Nottingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I142263535","host_organization_name":"University of Nottingham","host_organization_lineage":["https://openalex.org/I142263535"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Presentation / Conference Contribution"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W312096586","https://openalex.org/W1682014118","https://openalex.org/W1784034334","https://openalex.org/W1847556889","https://openalex.org/W1964559371","https://openalex.org/W1988790447","https://openalex.org/W2030931924","https://openalex.org/W2036234778","https://openalex.org/W2066063175","https://openalex.org/W2082279589","https://openalex.org/W2115998893","https://openalex.org/W2117866632","https://openalex.org/W2130945052","https://openalex.org/W2132424470","https://openalex.org/W2269949114","https://openalex.org/W2559671899","https://openalex.org/W2588353279","https://openalex.org/W2611144886","https://openalex.org/W2625548508","https://openalex.org/W2748167191","https://openalex.org/W2804579982","https://openalex.org/W2896731962","https://openalex.org/W2912934387","https://openalex.org/W2978631110","https://openalex.org/W2998768810","https://openalex.org/W3043771091","https://openalex.org/W3120740533","https://openalex.org/W4212883601","https://openalex.org/W6679625083","https://openalex.org/W6730228503","https://openalex.org/W6733419175","https://openalex.org/W6780661845"],"related_works":["https://openalex.org/W4382315444","https://openalex.org/W4233259193","https://openalex.org/W2600353413","https://openalex.org/W4390916549","https://openalex.org/W2915435096","https://openalex.org/W4381298925","https://openalex.org/W4285213578","https://openalex.org/W2097856925","https://openalex.org/W1997565450","https://openalex.org/W4298012357"],"abstract_inverted_index":{"Aggregation":[0],"operators":[1],"are":[2,17,134,223],"mathematical":[3],"functions":[4],"that":[5],"enable":[6],"the":[7,34,38,50,59,93,95,127,149,173,183,188,191,198,203,212,216,235],"fusion":[8,108,118,176,220],"of":[9,36,53,129,152,165,190],"information":[10,24,56,80,139],"from":[11,49,228],"multiple":[12],"sources.":[13],"Fuzzy":[14,29],"Integrals":[15],"(FIs)":[16],"widely":[18],"used":[19],"aggregation":[20,99],"operators,":[21],"which":[22,32,104,133],"combine":[23],"in":[25,109,120,122,178],"respect":[26,110],"to":[27,58,64,85,92,111,148,160,171,234,241],"a":[28,73,112,168],"Measure":[30],"(FM)":[31],"captures":[33],"worth":[35],"both":[37,211],"individual":[39],"sources":[40],"and":[41,88,143,202,215,232,251],"all":[42],"their":[43],"possible":[44,219],"combinations.":[45],"However,":[46],"FIs":[47],"suffer":[48],"potential":[51],"drawback":[52],"not":[54],"fusing":[55],"according":[57],"intuitively":[60],"interpretable":[61],"FM,":[62],"leading":[63],"non-intuitive":[65],"results.":[66],"The":[67],"latter":[68],"is":[69],"particularly":[70],"relevant":[71],"when":[72],"FM":[74,170],"has":[75],"been":[76],"defined":[77],"using":[78],"external":[79,138],"(e.g.":[81,140],"experts).":[82],"In":[83],"order":[84],"address":[86],"this":[87,123],"provide":[89,145],"an":[90,116,146,167],"alternative":[91,117,147],"FI,":[94],"Recursive":[96],"Average":[97],"(RAV)":[98],"operator":[100,119],"was":[101],"recently":[102,199],"proposed":[103],"enables":[105],"intuitive":[106],"data":[107],"given":[113],"FM.":[114],"With":[115],"place,":[121],"paper,":[124],"we":[125],"define":[126],"concept":[128],"`A":[130],"Priori'":[131],"FMs":[132],"generated":[135],"based":[136],"on":[137],"classification":[141],"accuracy)":[142],"thus":[144],"traditional":[150],"approaches":[151],"learning":[153,230],"or":[154],"manually":[155],"specifying":[156],"FMs.":[157],"We":[158,181],"proceed":[159],"develop":[161],"one":[162],"specific":[163],"instance":[164],"such":[166,245],"priori":[169],"support":[172],"decision":[174],"level":[175],"step":[177],"ensemble":[179,192,243],"classification.":[180],"evaluate":[182],"resulting":[184],"approach":[185],"by":[186,209,238],"contrasting":[187],"performance":[189],"classifiers":[193,244],"for":[194,225],"different":[195],"FMs,":[196],"including":[197],"introduced":[200],"Uriz":[201],"Sugeno":[204],"\u03bb-measure;":[205],"as":[206,208,218,246],"well":[207],"employing":[210],"Choquet":[213],"FI":[214],"RAV":[217],"operators.":[221],"Results":[222],"presented":[224],"20":[226],"datasets":[227],"machine":[229],"repositories":[231],"contextualised":[233],"wider":[236],"literature":[237],"comparing":[239],"them":[240],"state-of-the-art":[242],"Adaboost,":[247],"Bagging,":[248],"Random":[249],"Forest":[250],"Majority":[252],"Voting.":[253]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}