{"id":"https://openalex.org/W4285276674","doi":"https://doi.org/10.1109/tkde.2022.3177246","title":"Hierarchical Feature Selection Based on Label Distribution Learning","display_name":"Hierarchical Feature Selection Based on Label Distribution Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285276674","doi":"https://doi.org/10.1109/tkde.2022.3177246"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2022.3177246","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3177246","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":"https://openalex.org/A5078988375","display_name":"Yaojin Lin","orcid":"https://orcid.org/0000-0002-6749-9534"},"institutions":[{"id":"https://openalex.org/I9356336","display_name":"Zhangzhou Normal University","ror":"https://ror.org/02vj1vm13","country_code":"CN","type":"education","lineage":["https://openalex.org/I9356336"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaojin Lin","raw_affiliation_strings":["School of Computer Science, Minnan Normal University, 58299 Zhangzhou, Fujian, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Minnan Normal University, 58299 Zhangzhou, Fujian, China","institution_ids":["https://openalex.org/I9356336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016541910","display_name":"Haoyang Liu","orcid":"https://orcid.org/0000-0001-7482-8183"},"institutions":[{"id":"https://openalex.org/I9356336","display_name":"Zhangzhou Normal University","ror":"https://ror.org/02vj1vm13","country_code":"CN","type":"education","lineage":["https://openalex.org/I9356336"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyang Liu","raw_affiliation_strings":["School of Computer Science, Minnan Normal University, 58299 Zhangzhou, Fujian, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Minnan Normal University, 58299 Zhangzhou, Fujian, China","institution_ids":["https://openalex.org/I9356336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100608331","display_name":"Hong Zhao","orcid":"https://orcid.org/0000-0001-5448-8775"},"institutions":[{"id":"https://openalex.org/I9356336","display_name":"Zhangzhou Normal University","ror":"https://ror.org/02vj1vm13","country_code":"CN","type":"education","lineage":["https://openalex.org/I9356336"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Zhao","raw_affiliation_strings":["School of Computer Science, Minnan Normal University, 58299 Zhangzhou, Fujian, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Minnan Normal University, 58299 Zhangzhou, Fujian, China","institution_ids":["https://openalex.org/I9356336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056686459","display_name":"Qinghua Hu","orcid":"https://orcid.org/0000-0001-7765-8095"},"institutions":[{"id":"https://openalex.org/I132369690","display_name":"Tianjin University of Science and Technology","ror":"https://ror.org/018rbtf37","country_code":"CN","type":"education","lineage":["https://openalex.org/I132369690"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Hu","raw_affiliation_strings":["Computer Science and Technology, Tianjin University, Tianjin, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Computer Science and Technology, Tianjin University, Tianjin, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084641325","display_name":"Xingquan Zhu","orcid":"https://orcid.org/0000-0003-4129-9611"},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xingquan Zhu","raw_affiliation_strings":["Dept. of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, United States"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, United States","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080738591","display_name":"Xindong Wu","orcid":"https://orcid.org/0000-0003-2396-1704"},"institutions":[{"id":"https://openalex.org/I111236770","display_name":"University of Vermont","ror":"https://ror.org/0155zta11","country_code":"US","type":"education","lineage":["https://openalex.org/I111236770"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xindong Wu","raw_affiliation_strings":["Computer Science, University of Vermont, Burlington, Vermont, United States, 05405"],"affiliations":[{"raw_affiliation_string":"Computer Science, University of Vermont, Burlington, Vermont, United States, 05405","institution_ids":["https://openalex.org/I111236770"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5078988375"],"corresponding_institution_ids":["https://openalex.org/I9356336"],"apc_list":null,"apc_paid":null,"fwci":9.1697,"has_fulltext":false,"cited_by_count":67,"citation_normalized_percentile":{"value":0.98233099,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9990000128746033,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9990000128746033,"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":0.9958999752998352,"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/T10057","display_name":"Face and Expression Recognition","score":0.9818000197410583,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.761622428894043},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7286843657493591},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7249889373779297},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7228213548660278},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6596439480781555},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5906076431274414},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5832907557487488},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5617457032203674},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.44977471232414246},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41797730326652527},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3699517250061035},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22889041900634766}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.761622428894043},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7286843657493591},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7249889373779297},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7228213548660278},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6596439480781555},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5906076431274414},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5832907557487488},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5617457032203674},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.44977471232414246},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41797730326652527},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3699517250061035},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22889041900634766},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2022.3177246","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3177246","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/10","display_name":"Reduced inequalities","score":0.75}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W207202567","https://openalex.org/W254202083","https://openalex.org/W1565746575","https://openalex.org/W1583700199","https://openalex.org/W1685464609","https://openalex.org/W1976035027","https://openalex.org/W2008835805","https://openalex.org/W2016944307","https://openalex.org/W2046074173","https://openalex.org/W2048741410","https://openalex.org/W2061554433","https://openalex.org/W2106131881","https://openalex.org/W2108598243","https://openalex.org/W2115610681","https://openalex.org/W2150766729","https://openalex.org/W2165644552","https://openalex.org/W2185967890","https://openalex.org/W2241072627","https://openalex.org/W2246163430","https://openalex.org/W2330485005","https://openalex.org/W2343790552","https://openalex.org/W2400814742","https://openalex.org/W2494454509","https://openalex.org/W2604561167","https://openalex.org/W2740889030","https://openalex.org/W2803217490","https://openalex.org/W2804385132","https://openalex.org/W2904828343","https://openalex.org/W2925608410","https://openalex.org/W2940009787","https://openalex.org/W2966460909","https://openalex.org/W2970941190","https://openalex.org/W2976049311","https://openalex.org/W2997488121","https://openalex.org/W3034197595","https://openalex.org/W3034504038","https://openalex.org/W3035396932","https://openalex.org/W3090314981","https://openalex.org/W3101696129","https://openalex.org/W3120689699","https://openalex.org/W3120983706","https://openalex.org/W3154564794","https://openalex.org/W3155575086","https://openalex.org/W3179257852","https://openalex.org/W3184825405","https://openalex.org/W3196105425","https://openalex.org/W6609644794","https://openalex.org/W6633774736","https://openalex.org/W6637461618","https://openalex.org/W6684671274","https://openalex.org/W6764733053","https://openalex.org/W6798760447"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W3040691452","https://openalex.org/W4295122168","https://openalex.org/W3155717344","https://openalex.org/W1770458422","https://openalex.org/W2510961579","https://openalex.org/W4386564352","https://openalex.org/W2952668426"],"abstract_inverted_index":{"Hierarchical":[0],"classification":[1,17,87,159],"learning,":[2],"which":[3],"organizes":[4],"data":[5,23,178],"categories":[6,95,102],"into":[7],"a":[8,56,81,130,140],"hierarchical":[9,28,86,98,127,131],"structure,":[10,100],"is":[11,31,72,144],"an":[12],"effective":[13],"approach":[14],"for":[15,85,108],"large-scale":[16],"tasks.":[18],"The":[19,70],"high":[20],"dimensionality":[21],"of":[22,33,119,121,183,188],"feature":[24,57,83,142,153,172],"space,":[25],"represented":[26],"in":[27,96,114],"class":[29,41,46,76,94],"structures,":[30],"one":[32],"the":[34,40,67,75,97,117,148,163,186,189],"main":[35],"research":[36],"challenges.":[37,69],"In":[38,51],"addition,":[39],"hierarchy":[42],"often":[43],"introduces":[44],"imbalanced":[45],"distributions":[47],"and":[48,79,151,167],"causes":[49],"overfitting.":[50],"this":[52,136],"paper,":[53],"we":[54,125],"propose":[55],"selection":[58,173],"method":[59],"based":[60],"on":[61,175],"label":[62,132],"distribution":[63,133],"learning":[64,110],"to":[65,73,90,129,134,155],"address":[66],"above":[68],"crux":[71],"alleviate":[74],"imbalance":[77],"problem":[78,118],"learn":[80],"discriminative":[82,141],"subset":[84,143],"process.":[88],"Due":[89],"correlation":[91],"between":[92],"different":[93,181],"tree":[99],"sibling":[101],"can":[103,161],"provide":[104],"additional":[105],"supervisory":[106],"information":[107],"each":[109],"sub":[111],"tasks,":[112],"which,":[113],"turn,":[115],"alleviates":[116],"under-sampling":[120],"minority":[122],"categories.":[123],"Therefore,":[124],"transform":[126],"labels":[128],"represent":[135],"correlation.":[137],"After":[138],"that,":[139],"selected":[145],"recursively,":[146],"by":[147],"common":[149],"features":[150],"label-specific":[152],"constraints,":[154],"ensure":[156],"that":[157],"downstream":[158],"tasks":[160],"achieve":[162],"best":[164],"performance.":[165],"Experiments":[166],"comparisons,":[168],"using":[169],"seven":[170],"well-established":[171],"algorithms":[174],"six":[176],"real":[177],"sets":[179],"with":[180],"degrees":[182],"imbalance,":[184],"demonstrate":[185],"superiority":[187],"proposed":[190],"method.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":2}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
