{"id":"https://openalex.org/W3192022969","doi":"https://doi.org/10.1109/tkde.2021.3102120","title":"Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop","display_name":"Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3192022969","doi":"https://doi.org/10.1109/tkde.2021.3102120","mag":"3192022969"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2021.3102120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2021.3102120","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/A5100380588","display_name":"Wei Fan","orcid":"https://orcid.org/0009-0008-1900-7081"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Fan","raw_affiliation_strings":["Department of Computer Science, University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100786547","display_name":"Kunpeng Liu","orcid":"https://orcid.org/0000-0002-6053-5977"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kunpeng Liu","raw_affiliation_strings":["Department of Computer Science, University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458897","display_name":"Hao Liu","orcid":"https://orcid.org/0000-0003-4271-1567"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Hao Liu","raw_affiliation_strings":["Hong Kong University of Science and Technology, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Hong Kong, China","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101744165","display_name":"Yong Ge","orcid":"https://orcid.org/0000-0001-8094-4180"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yong Ge","raw_affiliation_strings":["Eller College of Management, University of Arizona, Tucson, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eller College of Management, University of Arizona, Tucson, AZ, USA","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101862104","display_name":"Hui Xiong","orcid":"https://orcid.org/0000-0001-6016-6465"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Xiong","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032187620","display_name":"Yanjie Fu","orcid":"https://orcid.org/0000-0002-1767-8024"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjie Fu","raw_affiliation_strings":["Department of Computer Science, University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2756,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.93182856,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9987999796867371,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9987999796867371,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9986000061035156,"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/T12535","display_name":"Machine Learning and Data Classification","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7939882278442383},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7316536903381348},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6879683136940002},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6310815215110779},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5672182440757751},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5656945109367371},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5435980558395386},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5002787113189697},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4139467477798462}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7939882278442383},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7316536903381348},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6879683136940002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6310815215110779},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5672182440757751},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5656945109367371},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5435980558395386},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5002787113189697},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4139467477798462},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tkde.2021.3102120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2021.3102120","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"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-124971","is_oa":false,"landing_page_url":"https://repository.hkust.edu.hk/ir/Record/1783.1-124971","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1739217412","display_name":"Collaborative Research: SHF: Small: Decentralized Edge Computing Platform for Privacy-Preserving Mobile Crowdsensing","funder_award_id":"2006889","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4124063972","display_name":"CRII: III: Understanding Urban Vibrancy: A Geographical Learning Approach Employing Big Crowd-Sourced Geo-Tagged Data","funder_award_id":"1755946","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5207648842","display_name":"CAREER:  Reinforced Imitative Graph Learning: Bridging the Gap between Perception and Prescription in Graph Sequences","funder_award_id":"2045567","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7900673000","display_name":null,"funder_award_id":"I2040950","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W1491953704","https://openalex.org/W1523989055","https://openalex.org/W1574447377","https://openalex.org/W1967030981","https://openalex.org/W1969685488","https://openalex.org/W1975777241","https://openalex.org/W1983801113","https://openalex.org/W2017337590","https://openalex.org/W2040584032","https://openalex.org/W2045615988","https://openalex.org/W2092829070","https://openalex.org/W2117423905","https://openalex.org/W2125212374","https://openalex.org/W2135046866","https://openalex.org/W2142222368","https://openalex.org/W2145339207","https://openalex.org/W2151170651","https://openalex.org/W2154053567","https://openalex.org/W2600702321","https://openalex.org/W2792217087","https://openalex.org/W2799784543","https://openalex.org/W2905510914","https://openalex.org/W2953043480","https://openalex.org/W2963432546","https://openalex.org/W2963653811","https://openalex.org/W2964015378","https://openalex.org/W3081189998","https://openalex.org/W3120740533","https://openalex.org/W3126977517","https://openalex.org/W3127251420","https://openalex.org/W4241931738","https://openalex.org/W4254755460","https://openalex.org/W4298857966","https://openalex.org/W6602038105","https://openalex.org/W6604963999","https://openalex.org/W6636914306","https://openalex.org/W6637967152","https://openalex.org/W6696265566","https://openalex.org/W6717827561","https://openalex.org/W6726873649","https://openalex.org/W6730844258"],"related_works":["https://openalex.org/W4362501864","https://openalex.org/W4306904969","https://openalex.org/W4380318855","https://openalex.org/W2138720691","https://openalex.org/W2031695474","https://openalex.org/W2586732548","https://openalex.org/W2786094008","https://openalex.org/W3131501806","https://openalex.org/W2799683370","https://openalex.org/W2807745940"],"abstract_inverted_index":{"We":[0,235],"study":[1],"the":[2,50,54,65,84,132,189,199,217,255,258,261,310,331],"problem":[3],"of":[4,135,225,232,242,279,314,334],"balancing":[5],"effectiveness":[6,87],"and":[7,74,88,154,164,179,228,257,305],"efficiency":[8,75,89],"in":[9,317],"automated":[10],"feature":[11,20,24,29,39,57,61,102,117,176,185,201,219,263,285],"selection.":[12],"Feature":[13],"selection":[14,30,40,58,118,177],"is":[15,43,69,171,181,207,265],"to":[16,48,63,98,115,157,172,182,188,209,248,267,303,329],"find":[17],"an":[18,174,222],"optimal":[19],"subset":[21,220],"from":[22,78,195,253],"large":[23],"space.":[25],"After":[26],"exploring":[27],"many":[28],"methods,":[31],"we":[32,82,96,110,149,215,275,297,322],"observe":[33],"a":[34,100,151,229,237,269,295],"computational":[35],"dilemma:":[36],"1)":[37],"traditional":[38],"(e.g.,":[41,139],"mRMR)":[42],"mostly":[44],"efficient,":[45],"but":[46,68],"difficult":[47],"identify":[49],"best":[51,66],"subset;":[52],"2)":[53],"emerging":[55],"reinforced":[56],"automatically":[59],"navigates":[60],"space":[62,103],"search":[64],"subset,":[67],"usually":[70],"inefficient.":[71],"Are":[72],"automation":[73],"always":[76],"apart":[77],"each":[79,315],"other?":[80],"Can":[81],"bridge":[83],"gap":[85],"between":[86],"under":[90],"automation?":[91],"Motivated":[92],"by":[93,119,130,204],"this":[94,146],"dilemma,":[95],"aim":[97],"develop":[99,268],"novel":[101,152],"navigation":[104],"method.":[105,336],"In":[106,145,213,273,287],"our":[107,335],"preliminary":[108,124],"work,":[109],"leveraged":[111,208],"interactive":[112,153,160,175],"reinforcement":[113,161],"learning":[114,143,162],"accelerate":[116],"external":[120],"trainer-agent":[121],"interaction.":[122],"Our":[123],"work":[125],"can":[126,292],"be":[127,294],"significantly":[128],"improved":[129,332],"modeling":[131],"structured":[133,184],"knowledge":[134,186],"its":[136],"downstream":[137],"task":[138],"decision":[140,165,205,233,283],"tree)":[141],"as":[142,221],"feedback.":[144],"journal":[147],"version,":[148],"propose":[150,236],"closed-loop":[155],"architecture":[156],"simultaneously":[158],"model":[159],"(IRL)":[163],"tree":[166,206,231,284],"feedback":[167],"(DTF).":[168],"Specifically,":[169],"IRL":[170,194],"create":[173],"loop":[178],"DTF":[180,192],"feed":[183],"back":[187],"loop.":[190],"The":[191],"improves":[193],"two":[196],"aspects.":[197],"First,":[198],"tree-structured":[200,262],"hierarchy":[202,264],"generated":[203],"improve":[210],"state":[211,251],"representation.":[212],"particular,":[214,274],"represent":[216],"selected":[218,311],"undirected":[223],"graph":[224,256],"feature-feature":[226],"correlations":[227],"directed":[230],"features.":[234],"new":[238,270,300],"embedding":[239],"method":[240],"capable":[241],"empowering":[243],"Graph":[244],"Convolutional":[245],"Network":[246],"(GCN)":[247],"jointly":[249],"learn":[250],"representation":[252],"both":[254],"tree.":[259],"Second,":[260],"exploited":[266],"reward":[271,277,301,307],"scheme.":[272],"personalize":[276],"assignment":[278],"agents":[280],"based":[281,308],"on":[282,309],"importance.":[286],"addition,":[288],"observing":[289],"agents\u2019":[290],"actions":[291],"also":[293],"feedback,":[296],"devise":[298],"another":[299],"scheme,":[302],"weigh":[304],"assign":[306],"frequency":[312],"ratio":[313],"agent":[316],"historical":[318],"action":[319],"records.":[320],"Finally,":[321],"present":[323],"extensive":[324],"experiments":[325],"with":[326],"real-world":[327],"datasets":[328],"demonstrate":[330],"performances":[333]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
