{"id":"https://openalex.org/W2553839529","doi":"https://doi.org/10.1109/fuzz-ieee.2016.7737788","title":"Fuzzy rough sets for self-labelling: An exploratory analysis","display_name":"Fuzzy rough sets for self-labelling: An exploratory analysis","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2553839529","doi":"https://doi.org/10.1109/fuzz-ieee.2016.7737788","mag":"2553839529"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz-ieee.2016.7737788","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2016.7737788","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.aber.ac.uk/portal/en/publications/fuzzy-rough-sets-for-selflabelling-an-exploratory-analysis(ae8645bc-6c5a-4c3b-a5d2-154617abdc00).html","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081322718","display_name":"Sarah Vluymans","orcid":"https://orcid.org/0000-0003-1782-8114"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]},{"id":"https://openalex.org/I4210139803","display_name":"VIB-UGent Center for Inflammation Research","ror":"https://ror.org/04q4ydz28","country_code":"BE","type":"facility","lineage":["https://openalex.org/I2802017950","https://openalex.org/I32597200","https://openalex.org/I4210139803"]},{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE","ES"],"is_corresponding":true,"raw_author_name":"Sarah Vluymans","raw_affiliation_strings":["Department of Applied Mathematics, Ghent University, Ghent, Belgium","Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain","VIB Inflammation Research Center, Zwiinaarde, Belgium"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Ghent University, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]},{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]},{"raw_affiliation_string":"VIB Inflammation Research Center, Zwiinaarde, Belgium","institution_ids":["https://openalex.org/I4210139803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035819717","display_name":"Neil Mac Parthal\u00e1in","orcid":"https://orcid.org/0000-0003-1935-2914"},"institutions":[{"id":"https://openalex.org/I16038530","display_name":"Aberystwyth University","ror":"https://ror.org/015m2p889","country_code":"GB","type":"education","lineage":["https://openalex.org/I16038530"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Neil Mac Parthalain","raw_affiliation_strings":["Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, Wales, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, Wales, UK","institution_ids":["https://openalex.org/I16038530"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091486233","display_name":"Chris Cornelis","orcid":"https://orcid.org/0000-0002-6852-4041"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]},{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["BE","ES"],"is_corresponding":false,"raw_author_name":"Chris Cornelis","raw_affiliation_strings":["Department of Applied Mathematics, Ghent University, Ghent, Belgium","Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics, Ghent University, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]},{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001585713","display_name":"Yvan Saeys","orcid":"https://orcid.org/0000-0002-0415-1506"},"institutions":[{"id":"https://openalex.org/I2801227569","display_name":"Ghent University Hospital","ror":"https://ror.org/00xmkp704","country_code":"BE","type":"healthcare","lineage":["https://openalex.org/I2801227569"]},{"id":"https://openalex.org/I4210139803","display_name":"VIB-UGent Center for Inflammation Research","ror":"https://ror.org/04q4ydz28","country_code":"BE","type":"facility","lineage":["https://openalex.org/I2802017950","https://openalex.org/I32597200","https://openalex.org/I4210139803"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Yvan Saeys","raw_affiliation_strings":["Department of Respiratory Medicine, Ghent University, Ghent, Belgium","VIB Inflammation Research Center, Zwiinaarde, Belgium"],"affiliations":[{"raw_affiliation_string":"Department of Respiratory Medicine, Ghent University, Ghent, Belgium","institution_ids":["https://openalex.org/I2801227569"]},{"raw_affiliation_string":"VIB Inflammation Research Center, Zwiinaarde, Belgium","institution_ids":["https://openalex.org/I4210139803"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081322718"],"corresponding_institution_ids":["https://openalex.org/I173304897","https://openalex.org/I32597200","https://openalex.org/I4210139803"],"apc_list":null,"apc_paid":null,"fwci":0.3594,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.69610489,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"931","last_page":"938"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9526000022888184,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9474999904632568,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.7031362056732178},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6617820262908936},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5866042375564575},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5850784778594971},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5734817981719971},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5573896169662476},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.5523177981376648},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5182421207427979},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5057370662689209},{"id":"https://openalex.org/keywords/dominance-based-rough-set-approach","display_name":"Dominance-based rough set approach","score":0.4753912687301636},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4599217176437378},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4529409408569336},{"id":"https://openalex.org/keywords/labelling","display_name":"Labelling","score":0.444333553314209},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4396190643310547},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42404812574386597},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3644958734512329},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10252439975738525}],"concepts":[{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.7031362056732178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6617820262908936},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5866042375564575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5850784778594971},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5734817981719971},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5573896169662476},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.5523177981376648},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5182421207427979},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5057370662689209},{"id":"https://openalex.org/C39105242","wikidata":"https://www.wikidata.org/wiki/Q5290286","display_name":"Dominance-based rough set approach","level":3,"score":0.4753912687301636},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4599217176437378},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4529409408569336},{"id":"https://openalex.org/C2780523633","wikidata":"https://www.wikidata.org/wiki/Q380709","display_name":"Labelling","level":2,"score":0.444333553314209},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4396190643310547},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42404812574386597},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3644958734512329},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10252439975738525},{"id":"https://openalex.org/C73484699","wikidata":"https://www.wikidata.org/wiki/Q161733","display_name":"Criminology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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":3,"locations":[{"id":"doi:10.1109/fuzz-ieee.2016.7737788","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2016.7737788","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},{"id":"pmh:oai:aber.ac.uk:publications/ae8645bc-6c5a-4c3b-a5d2-154617abdc00","is_oa":true,"landing_page_url":null,"pdf_url":"https://pure.aber.ac.uk/portal/en/publications/fuzzy-rough-sets-for-selflabelling-an-exploratory-analysis(ae8645bc-6c5a-4c3b-a5d2-154617abdc00).html","source":{"id":"https://openalex.org/S4306401660","display_name":"Aberystwyth Research portal (Aberystwyth University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I16038530","host_organization_name":"Aberystwyth University","host_organization_lineage":["https://openalex.org/I16038530"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:archive.ugent.be:8510056","is_oa":false,"landing_page_url":"http://hdl.handle.net/1854/LU-8510056","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9781509006250","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:aber.ac.uk:publications/ae8645bc-6c5a-4c3b-a5d2-154617abdc00","is_oa":true,"landing_page_url":null,"pdf_url":"https://pure.aber.ac.uk/portal/en/publications/fuzzy-rough-sets-for-selflabelling-an-exploratory-analysis(ae8645bc-6c5a-4c3b-a5d2-154617abdc00).html","source":{"id":"https://openalex.org/S4306401660","display_name":"Aberystwyth Research portal (Aberystwyth University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I16038530","host_organization_name":"Aberystwyth University","host_organization_lineage":["https://openalex.org/I16038530"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G236970278","display_name":null,"funder_award_id":"P11-TIC-7765","funder_id":"https://openalex.org/F4320323737","funder_display_name":"Ministerio de Ciencia y Tecnolog\u00eda"},{"id":"https://openalex.org/G4016751739","display_name":null,"funder_award_id":"P10-TIC-6858","funder_id":"https://openalex.org/F4320323737","funder_display_name":"Ministerio de Ciencia y Tecnolog\u00eda"},{"id":"https://openalex.org/G7128395569","display_name":null,"funder_award_id":"TIN2014-57251-P","funder_id":"https://openalex.org/F4320323737","funder_display_name":"Ministerio de Ciencia y Tecnolog\u00eda"}],"funders":[{"id":"https://openalex.org/F4320322603","display_name":"Universiteit Gent","ror":"https://ror.org/00cv9y106"},{"id":"https://openalex.org/F4320323737","display_name":"Ministerio de Ciencia y Tecnolog\u00eda","ror":"https://ror.org/034900433"},{"id":"https://openalex.org/F4320335227","display_name":"Bijzonder Onderzoeksfonds UGent","ror":"https://ror.org/00cv9y106"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2553839529.pdf","grobid_xml":"https://content.openalex.org/works/W2553839529.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1600321904","https://openalex.org/W1990334093","https://openalex.org/W2002680690","https://openalex.org/W2027654459","https://openalex.org/W2060907774","https://openalex.org/W2076837108","https://openalex.org/W2089103660","https://openalex.org/W2098370488","https://openalex.org/W2106401878","https://openalex.org/W2130035779","https://openalex.org/W2131487297","https://openalex.org/W2154887800","https://openalex.org/W2165250079","https://openalex.org/W2169323880","https://openalex.org/W2211181684","https://openalex.org/W2287320286","https://openalex.org/W2912565176","https://openalex.org/W4211007335","https://openalex.org/W4255833381"],"related_works":["https://openalex.org/W2397494716","https://openalex.org/W2042497195","https://openalex.org/W2473601878","https://openalex.org/W2123662875","https://openalex.org/W2379884433","https://openalex.org/W2087213536","https://openalex.org/W4214891796","https://openalex.org/W2382975841","https://openalex.org/W2978519593","https://openalex.org/W2102746356"],"abstract_inverted_index":{"Semi-supervised":[0],"learning":[1],"incorporates":[2],"aspects":[3],"of":[4,28,61,65,75,83,107,138,150,172,190,201,204,211],"both":[5,173],"supervised":[6],"and":[7,73,175,183],"unsupervised":[8],"learning.":[9],"In":[10,68],"semi-supervised":[11,29,112,212],"classification,":[12],"only":[13,121],"some":[14],"data":[15,47,109,124,131],"instances":[16,110,125,132],"have":[17],"associated":[18],"class":[19,42,55],"labels,":[20],"while":[21],"others":[22],"are":[23,32,116],"unlabelled.":[24],"One":[25],"particular":[26],"group":[27],"classification":[30,213],"approaches":[31],"those":[33],"known":[34],"as":[35,133],"self-labelling":[36,84,194],"techniques,":[37],"which":[38],"attempt":[39],"to":[40,44,143],"assign":[41],"labels":[43],"the":[45,54,59,62,66,71,81,105,122,129,139,148,151,160,188,202,209],"unlabelled":[46,108,130],"instances.":[48],"This":[49],"is":[50,85,92],"achieved":[51],"by":[52,119,127],"using":[53],"predictions":[56,115,140],"based":[57,164],"upon":[58],"information":[60],"labelled":[63,123],"part":[64],"data.":[67],"this":[69],"paper,":[70],"applicability":[72,203],"suitability":[74],"fuzzy":[76,99,153,165,192,205],"rough":[77,100,154,166,193,206],"set":[78,101],"theory":[79],"for":[80,111,208],"task":[82,210],"investigated.":[86],"An":[87],"important":[88],"preparatory":[89],"experimental":[90],"study":[91,157],"presented":[93],"that":[94,159,185],"evaluates":[95],"how":[96],"accurately":[97],"different":[98,152],"models":[102],"can":[103],"predict":[104],"classes":[106],"classification.":[113],"The":[114],"made":[117],"either":[118],"considering":[120],"or":[126],"involving":[128],"well.":[134],"A":[135],"stability":[136],"analysis":[137],"also":[141,197],"helps":[142],"provide":[144,198],"further":[145],"insight":[146],"into":[147],"characteristics":[149],"models.":[155],"Our":[156,177],"shows":[158],"ordered":[161],"weighted":[162],"average":[163],"model":[167],"performs":[168],"best":[169],"in":[170,214],"terms":[171],"accuracy":[174],"stability.":[176],"conclusions":[178],"offer":[179],"a":[180,191],"solid":[181],"foundation":[182],"rationale":[184],"will":[186],"allow":[187],"construction":[189],"technique.":[195],"They":[196],"an":[199],"understanding":[200],"sets":[207],"general.":[215]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
