{"id":"https://openalex.org/W3081769274","doi":"https://doi.org/10.1109/fuzz48607.2020.9177820","title":"Fuzzy Set Similarity for Feature Selection in Classification","display_name":"Fuzzy Set Similarity for Feature Selection in Classification","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3081769274","doi":"https://doi.org/10.1109/fuzz48607.2020.9177820","mag":"3081769274"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz48607.2020.9177820","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz48607.2020.9177820","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},"type":"article","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/A5028656638","display_name":"Valerie Cross","orcid":null},"institutions":[{"id":"https://openalex.org/I83328450","display_name":"Miami University","ror":"https://ror.org/05nbqxr67","country_code":"US","type":"education","lineage":["https://openalex.org/I83328450"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Valerie Cross","raw_affiliation_strings":["Computer Science and Software Engineering, Miami University, Oxford, OH, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Software Engineering, Miami University, Oxford, OH, USA","institution_ids":["https://openalex.org/I83328450"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045139570","display_name":"Michael A. Zmuda","orcid":null},"institutions":[{"id":"https://openalex.org/I83328450","display_name":"Miami University","ror":"https://ror.org/05nbqxr67","country_code":"US","type":"education","lineage":["https://openalex.org/I83328450"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Zmuda","raw_affiliation_strings":["Computer Science and Software Engineering, Miami University, Oxford, OH, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Software Engineering, Miami University, Oxford, OH, USA","institution_ids":["https://openalex.org/I83328450"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006513001","display_name":"Rahul Paul","orcid":"https://orcid.org/0000-0003-1491-1166"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rahul Paul","raw_affiliation_strings":["Computer Science and Engineering, University of South Florida, Tampa, FL, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000168449","display_name":"Lawrence Hall","orcid":"https://orcid.org/0000-0002-7898-8456"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence Hall","raw_affiliation_strings":["Computer Science and Engineering, University of South Florida, Tampa, FL, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5028656638"],"corresponding_institution_ids":["https://openalex.org/I83328450"],"apc_list":null,"apc_paid":null,"fwci":0.1954,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49500907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9958999752998352,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9958999752998352,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9911999702453613,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6596745252609253},{"id":"https://openalex.org/keywords/concordance","display_name":"Concordance","score":0.658332109451294},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5975807905197144},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5888895988464355},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5616731643676758},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.5183592438697815},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.49553048610687256},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.492404580116272},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.48939648270606995},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4851287305355072},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.48264914751052856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47411635518074036},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.46968844532966614},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.44280779361724854},{"id":"https://openalex.org/keywords/concordance-correlation-coefficient","display_name":"Concordance correlation coefficient","score":0.43630513548851013},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4299914538860321},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.4169575572013855},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.39526936411857605},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3618508577346802},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25105804204940796}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6596745252609253},{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.658332109451294},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5975807905197144},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5888895988464355},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5616731643676758},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.5183592438697815},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.49553048610687256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.492404580116272},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.48939648270606995},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4851287305355072},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.48264914751052856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47411635518074036},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.46968844532966614},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.44280779361724854},{"id":"https://openalex.org/C2781059462","wikidata":"https://www.wikidata.org/wiki/Q5158906","display_name":"Concordance correlation coefficient","level":2,"score":0.43630513548851013},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4299914538860321},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.4169575572013855},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.39526936411857605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3618508577346802},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25105804204940796},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz48607.2020.9177820","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz48607.2020.9177820","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2015795623","https://openalex.org/W2113242816","https://openalex.org/W2122410182","https://openalex.org/W2123402141","https://openalex.org/W2123822128","https://openalex.org/W2148143831","https://openalex.org/W2155345760","https://openalex.org/W2241990718","https://openalex.org/W2293600471","https://openalex.org/W2799061466","https://openalex.org/W2950016253","https://openalex.org/W2998216295","https://openalex.org/W4205687621","https://openalex.org/W4285719527","https://openalex.org/W6678087030","https://openalex.org/W7014191107","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W4256429076","https://openalex.org/W1971174658","https://openalex.org/W2099195351","https://openalex.org/W2320442256","https://openalex.org/W2348092930","https://openalex.org/W2012063272","https://openalex.org/W3129272755","https://openalex.org/W2138685729","https://openalex.org/W2052615004","https://openalex.org/W4399681812"],"abstract_inverted_index":{"A":[0],"problem":[1],"for":[2,54],"machine":[3],"learning":[4],"research":[5],"occurs":[6],"when":[7],"many":[8],"possible":[9],"features":[10,53,60,79,138],"exist":[11],"but":[12],"the":[13,32,37,43,51,99,104,118,130,147,172,177,190,194,206,211,228,250],"training":[14,40],"data":[15,23,41,185,230],"examples":[16],"are":[17,89,187],"very":[18],"few.":[19],"For":[20],"example,":[21],"microarray":[22,182],"typically":[24],"have":[25],"a":[26,63,121,152],"much":[27],"larger":[28],"number":[29,38],"of":[30,39,65,83,98,106,157,165,196],"features,":[31],"genes,":[33],"as":[34,144,146,151,247,249],"compared":[35,162],"to":[36,48,58,75,128,192,215,221],"examples,":[42],"patients.":[44],"One":[45],"approach":[46],"is":[47,109,126,161,218],"first":[49],"determine":[50,193],"best":[52],"prediction":[55],"and":[56,102,169,201,232],"then":[57,103],"group":[59],"based":[61],"on":[62,227],"measure":[64,234],"their":[66],"relatedness.":[67],"The":[68,123,155],"concordance":[69,148,207,212,251],"correlation":[70,149,208,213,252],"coefficient":[71,150,214],"has":[72],"been":[73],"used":[74,110,127,188],"place":[76],"somewhat":[77],"correlated":[78],"into":[80],"disjoint":[81],"groups":[82,101],"similar":[84],"features.":[85],"Multiple":[86],"base":[87,107],"classifiers":[88,108,174],"created":[90,175],"by":[91],"randomly":[92],"picking":[93],"one":[94],"feature":[95,100],"from":[96],"each":[97],"collection":[105],"in":[111,117,163,189],"an":[112],"ensemble":[113,119,173],"classifier.":[114],"Each":[115],"classifier":[116],"provides":[120],"vote.":[122],"majority":[124],"vote":[125],"produce":[129],"final":[131],"class":[132],"prediction.":[133],"This":[134],"paper":[135],"investigates":[136],"grouping":[137],"using":[139,171],"fuzzy":[140,197,222,238],"set":[141,198,223,231,239],"similarity":[142,199,224,240],"measures":[143,160,200,241],"well":[145,248],"relatedness":[153,179],"measure.":[154],"performance":[156,233],"these":[158],"different":[159,178,237],"terms":[164],"accuracy,":[166],"sensitivity,":[167],"specificity,":[168],"F-measure":[170],"with":[176,205],"measures.":[180,225],"Four":[181],"gene":[183],"expression":[184],"sets":[186],"experiments":[191],"usefulness":[195],"how":[202],"they":[203],"compare":[204],"coefficient.":[209,253],"Using":[210],"guide":[216],"clustering":[217],"not":[219],"superior":[220],"Depending":[226],"particular":[229],"being":[235],"used,":[236],"perform":[242],"better":[243],"than":[244],"or":[245],"just":[246]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
