{"id":"https://openalex.org/W7084580536","doi":"https://doi.org/10.1109/tkde.2025.3617583","title":"Adaptive Hyper-Box Granulation With Justifiable Granularity for Feature Selection","display_name":"Adaptive Hyper-Box Granulation With Justifiable Granularity for Feature Selection","publication_year":2025,"publication_date":"2025-10-03","ids":{"openalex":"https://openalex.org/W7084580536","doi":"https://doi.org/10.1109/tkde.2025.3617583"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2025.3617583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3617583","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-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":null,"display_name":"Wentao Li","orcid":"https://orcid.org/0000-0002-7777-0818"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Li","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-7777-0818","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bowen Yang","orcid":"https://orcid.org/0009-0003-4226-5752"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Yang","raw_affiliation_strings":["College of Artificial Intelligence, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0009-0003-4226-5752","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Witold Pedrycz","orcid":"https://orcid.org/0000-0002-9335-9930"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Witold Pedrycz","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada"],"raw_orcid":"https://orcid.org/0000-0002-9335-9930","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chao Zhang","orcid":"https://orcid.org/0000-0001-6248-9962"},"institutions":[{"id":"https://openalex.org/I181877577","display_name":"Shanxi University","ror":"https://ror.org/03y3e3s17","country_code":"CN","type":"education","lineage":["https://openalex.org/I181877577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Zhang","raw_affiliation_strings":["School of Computer and Information Technology, Shanxi University, Taiyuan, China"],"raw_orcid":"https://orcid.org/0000-0001-6248-9962","affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Shanxi University, Taiyuan, China","institution_ids":["https://openalex.org/I181877577"]}]},{"author_position":"last","author":{"id":null,"display_name":"Tao Zhan","orcid":"https://orcid.org/0000-0003-2603-544X"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zhan","raw_affiliation_strings":["School of Mathematics and Statistics, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0003-2603-544X","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.9206,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.93690325,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"37","issue":"12","first_page":"6847","last_page":"6862"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12475","display_name":"Plant Parasitism and Resistance","score":0.9460999965667725,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12475","display_name":"Plant Parasitism and Resistance","score":0.9460999965667725,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11836","display_name":"Bryophyte Studies and Records","score":0.02070000022649765,"subfield":{"id":"https://openalex.org/subfields/1105","display_name":"Ecology, Evolution, Behavior and Systematics"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11879","display_name":"Protist diversity and phylogeny","score":0.00570000009611249,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8482000231742859},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.708899974822998},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5684000253677368},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.5145000219345093},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.51419997215271},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.4875999987125397},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.46860000491142273},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.45500001311302185},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.454800009727478}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8482000231742859},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7251999974250793},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.708899974822998},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6729999780654907},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.5145000219345093},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.51419997215271},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.4875999987125397},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.46860000491142273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4575999975204468},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.45500001311302185},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.454800009727478},{"id":"https://openalex.org/C17209119","wikidata":"https://www.wikidata.org/wiki/Q5596712","display_name":"Granular computing","level":3,"score":0.4341000020503998},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.42250001430511475},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.3968999981880188},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.35659998655319214},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.35010001063346863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3264999985694885},{"id":"https://openalex.org/C16811321","wikidata":"https://www.wikidata.org/wiki/Q17138905","display_name":"Minimum redundancy feature selection","level":3,"score":0.31540000438690186},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C44859942","wikidata":"https://www.wikidata.org/wiki/Q5426511","display_name":"FLAME clustering","level":5,"score":0.2750999927520752},{"id":"https://openalex.org/C88463166","wikidata":"https://www.wikidata.org/wiki/Q1543243","display_name":"Granulation","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2025.3617583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3617583","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.6052058339118958}],"awards":[{"id":"https://openalex.org/G1510598933","display_name":null,"funder_award_id":"62403393","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2244941088","display_name":null,"funder_award_id":"12201518","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3825846903","display_name":null,"funder_award_id":"CSTB2025NSCQ-GPX0512","funder_id":"https://openalex.org/F4320323172","funder_display_name":"Natural Science Foundation of Chongqing"},{"id":"https://openalex.org/G6118151116","display_name":null,"funder_award_id":"62272284","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323172","display_name":"Natural Science Foundation of Chongqing","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1575538451","https://openalex.org/W1981472734","https://openalex.org/W2018113971","https://openalex.org/W2059429344","https://openalex.org/W2153676086","https://openalex.org/W2180430750","https://openalex.org/W2316020619","https://openalex.org/W2344301817","https://openalex.org/W2536981762","https://openalex.org/W2550999023","https://openalex.org/W2582146055","https://openalex.org/W2743621318","https://openalex.org/W2809438551","https://openalex.org/W2922513649","https://openalex.org/W2944017292","https://openalex.org/W2955612236","https://openalex.org/W2967181455","https://openalex.org/W3047096399","https://openalex.org/W3174998861","https://openalex.org/W3211819425","https://openalex.org/W4233343814","https://openalex.org/W4283394375","https://openalex.org/W4289236186","https://openalex.org/W4289792606","https://openalex.org/W4312802726","https://openalex.org/W4313128165","https://openalex.org/W4315473677","https://openalex.org/W4319993347","https://openalex.org/W4361856335","https://openalex.org/W4387757542","https://openalex.org/W4388573262","https://openalex.org/W4394863099","https://openalex.org/W4394994616","https://openalex.org/W4399359815","https://openalex.org/W4400033057","https://openalex.org/W4403390116","https://openalex.org/W4410582735","https://openalex.org/W4411446385"],"related_works":[],"abstract_inverted_index":{"Clustering":[0],"as":[1],"a":[2,82,102,149],"fundamental":[3],"technique":[4],"in":[5,138],"data":[6,14,50,132],"mining":[7],"and":[8,43,54,62,105,129,175,181,187,210,217],"machine":[9],"learning,":[10],"aims":[11],"to":[12,64,70,114,145,163],"partition":[13],"into":[15],"meaningful":[16],"groups":[17],"based":[18,86],"on":[19,87,197],"the":[20,37,72,79,95,110,115,125,134,139,146,159,165,170,185,190,203,218],"inherent":[21],"relationships":[22],"among":[23],"data.":[24],"However,":[25],"traditional":[26],"clustering":[27,66,85,121,161,179],"algorithms":[28],"typically":[29],"assume":[30],"convex":[31],"hyperspherical":[32],"geometry":[33],"of":[34,97,117,127,136,148,172,189,205],"data,":[35,118],"where":[36],"clusters":[38],"have":[39],"clearly":[40],"defined":[41],"boundaries":[42],"do":[44],"not":[45],"overlap.":[46],"In":[47],"contrast,":[48],"real-world":[49],"often":[51],"exhibits":[52],"complex":[53],"non-convex":[55],"geometries,":[56],"which":[57,92],"makes":[58],"these":[59],"assumptions":[60],"ineffective":[61],"lead":[63],"inaccurate":[65],"results":[67,216],"that":[68,223],"fail":[69],"capture":[71],"intrinsic":[73],"structure.":[74],"To":[75,183],"address":[76],"this":[77],"challenge,":[78],"paper":[80],"proposes":[81],"novel":[83,150],"granular":[84,160],"an":[88],"enhanced":[89],"granularity":[90],"representation,":[91],"further":[93],"refines":[94],"principle":[96,162],"justifiable":[98],"granularity.":[99],"By":[100,123],"introducing":[101],"more":[103],"precise":[104],"flexible":[106],"hyper-box":[107,151],"granulation":[108],"mechanism,":[109],"method":[111],"dynamically":[112],"adapts":[113],"topology":[116],"thereby":[119],"improving":[120,178],"accuracy.":[122],"defining":[124],"degree":[126],"aggregation":[128],"discreteness":[130],"between":[131],"points,":[133],"importance":[135],"attributes":[137],"feature":[140,152,166,212,228],"space":[141],"is":[142],"quantified,":[143],"leading":[144],"design":[147],"selection":[153,167,213,229],"(HBFS)":[154],"algorithm.":[155],"This":[156],"algorithm":[157,207],"integrates":[158],"optimize":[164],"process,":[168],"reducing":[169],"impact":[171],"redundant":[173],"features":[174],"noise,":[176],"thus":[177],"efficiency":[180],"interpretability.":[182],"validate":[184],"superiority":[186],"effectiveness":[188],"proposed":[191],"method,":[192],"extensive":[193],"experiments":[194],"were":[195],"conducted":[196],"fifteen":[198],"publicly":[199],"available":[200],"datasets,":[201],"comparing":[202],"performance":[204],"HBFS":[206,224],"with":[208],"classical":[209],"state-of-art":[211],"methods.":[214],"The":[215],"statistical":[219],"significance":[220],"tests":[221],"show":[222],"significantly":[225],"outperforms":[226],"existing":[227],"methods":[230],"across":[231],"various":[232],"evaluation":[233],"metrics.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
