{"id":"https://openalex.org/W3090616938","doi":"https://doi.org/10.3390/e22101093","title":"Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets","display_name":"Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets","publication_year":2020,"publication_date":"2020-09-29","ids":{"openalex":"https://openalex.org/W3090616938","doi":"https://doi.org/10.3390/e22101093","mag":"3090616938","pmid":"https://pubmed.ncbi.nlm.nih.gov/33286862","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/7597188"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22101093","pdf_url":"https://www.mdpi.com/1099-4300/22/10/1093/pdf?version=1601361934","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/22/10/1093/pdf?version=1601361934","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033932345","display_name":"Muhammad Umar Chaudhry","orcid":"https://orcid.org/0000-0002-7287-2372"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Muhammad Umar Chaudhry","raw_affiliation_strings":["AiHawks, Multan 60000, Pakistan","Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea"],"affiliations":[{"raw_affiliation_string":"AiHawks, Multan 60000, Pakistan","institution_ids":[]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011835010","display_name":"Muhammad Yasir","orcid":"https://orcid.org/0000-0002-7106-6598"},"institutions":[{"id":"https://openalex.org/I4210147796","display_name":"University of Faisalabad","ror":"https://ror.org/04eps4h65","country_code":"PK","type":"education","lineage":["https://openalex.org/I4210147796"]},{"id":"https://openalex.org/I142732210","display_name":"University of Engineering and Technology Lahore","ror":"https://ror.org/0051w2v06","country_code":"PK","type":"education","lineage":["https://openalex.org/I142732210"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Muhammad Yasir","raw_affiliation_strings":["Department of Computer Science, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad 38000, Pakistan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad 38000, Pakistan","institution_ids":["https://openalex.org/I4210147796","https://openalex.org/I142732210"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044452098","display_name":"Muhammad Nabeel Asghar","orcid":"https://orcid.org/0000-0002-9487-4344"},"institutions":[{"id":"https://openalex.org/I127670440","display_name":"Bahauddin Zakariya University","ror":"https://ror.org/05x817c41","country_code":"PK","type":"education","lineage":["https://openalex.org/I127670440"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Muhammad Nabeel Asghar","raw_affiliation_strings":["Department of Computer Science, Bahauddin Zakariya University, Multan 60000, Pakistan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Bahauddin Zakariya University, Multan 60000, Pakistan","institution_ids":["https://openalex.org/I127670440"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067651075","display_name":"Jee-Hyong Lee","orcid":"https://orcid.org/0000-0001-7242-7677"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jee-Hyong Lee","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033932345","https://openalex.org/A5067651075"],"corresponding_institution_ids":["https://openalex.org/I848706","https://openalex.org/I848706"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165,"provenance":"doaj"},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165,"provenance":"doaj"},"fwci":0.122,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.479906,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":61,"max":69},"biblio":{"volume":"22","issue":"10","first_page":"1093","last_page":"1093"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Swarm Intelligence Optimization Algorithms","score":0.9974,"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/T10100","display_name":"Swarm Intelligence Optimization Algorithms","score":0.9974,"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/T11975","display_name":"Application of Genetic Programming in Machine Learning","score":0.9972,"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":"Learning with Noisy Labels in Machine Learning","score":0.9942,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6239308},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.61125827},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature Selection","score":0.591252},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-Objective Optimization","score":0.526378},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.523732},{"id":"https://openalex.org/keywords/nature-inspired-algorithms","display_name":"Nature-Inspired Algorithms","score":0.522523},{"id":"https://openalex.org/keywords/constraint-handling","display_name":"Constraint Handling","score":0.51841},{"id":"https://openalex.org/keywords/robust-learning","display_name":"Robust Learning","score":0.518243},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.42485934}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.75830805},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6662224},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6239308},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.61125827},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.523732},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.49971175},{"id":"https://openalex.org/C46149586","wikidata":"https://www.wikidata.org/wiki/Q11785332","display_name":"Monte Carlo tree search","level":3,"score":0.46673784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45575848},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44276214},{"id":"https://openalex.org/C197855036","wikidata":"https://www.wikidata.org/wiki/Q380172","display_name":"Binary tree","level":2,"score":0.4394943},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.43485734},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4295364},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.42485934},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39929342},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37454516},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.37316483},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24942154},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08060071},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22101093","pdf_url":"https://www.mdpi.com/1099-4300/22/10/1093/pdf?version=1601361934","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":false,"landing_page_url":"https://doaj.org/article/daf2a078d5244c88b1222f2a5a33058b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597188","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":["National Institutes of Health"],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33286862","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":["National Institutes of Health"],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22101093","pdf_url":"https://www.mdpi.com/1099-4300/22/10/1093/pdf?version=1601361934","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":43,"referenced_works":["https://openalex.org/W1158789509","https://openalex.org/W1480376833","https://openalex.org/W1495061682","https://openalex.org/W1543715688","https://openalex.org/W1967177998","https://openalex.org/W1968103139","https://openalex.org/W1975777241","https://openalex.org/W1980053379","https://openalex.org/W2013885787","https://openalex.org/W2017337590","https://openalex.org/W2020355555","https://openalex.org/W2027739797","https://openalex.org/W2066512999","https://openalex.org/W2083945868","https://openalex.org/W2087678375","https://openalex.org/W2088121750","https://openalex.org/W2092260586","https://openalex.org/W2101826491","https://openalex.org/W2117613928","https://openalex.org/W2124426131","https://openalex.org/W2126316555","https://openalex.org/W2147169507","https://openalex.org/W2166317455","https://openalex.org/W2257979135","https://openalex.org/W2312203001","https://openalex.org/W2434330801","https://openalex.org/W2546082031","https://openalex.org/W2585821407","https://openalex.org/W2588659412","https://openalex.org/W2604368534","https://openalex.org/W2767768852","https://openalex.org/W2787894218","https://openalex.org/W2790953952","https://openalex.org/W2804966426","https://openalex.org/W2805829984","https://openalex.org/W2806295587","https://openalex.org/W2899750879","https://openalex.org/W2902237356","https://openalex.org/W2972062361","https://openalex.org/W2998216295","https://openalex.org/W3103167018","https://openalex.org/W4244238212","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4296209631","https://openalex.org/W4247855592","https://openalex.org/W4226164546","https://openalex.org/W3097449145","https://openalex.org/W2766259847","https://openalex.org/W2740304877","https://openalex.org/W2571592646","https://openalex.org/W2567165815","https://openalex.org/W2561617217","https://openalex.org/W2112583639"],"abstract_inverted_index":{"The":[0,12,31,151],"complexity":[1],"and":[2,38,73,100,210],"high":[3],"dimensionality":[4,28],"are":[5,144,185],"the":[6,24,34,39,77,82,97,101,111,116,167,177,197,211],"inherent":[7],"concerns":[8],"of":[9,14,29,103,109,154,169,207],"big":[10],"data.":[11],"role":[13],"feature":[15,64,70,125,135,141,157,212],"selection":[16,65,71,136,142,158,213],"has":[17],"gained":[18],"prime":[19],"importance":[20],"to":[21],"cope":[22],"with":[23,90,199],"issue":[25],"by":[26,66],"reducing":[27],"datasets.":[30],"compromise":[32],"between":[33,96],"maximum":[35],"classification":[36,208],"accuracy":[37,209],"minimum":[40],"dimensions":[41],"is":[42,93,137,160],"as":[43],"yet":[44],"an":[45],"unsolved":[46],"puzzle.":[47],"Recently,":[48],"Monte":[49],"Carlo":[50],"Tree":[51],"Search":[52],"(MCTS)-based":[53],"techniques":[54],"have":[55,59],"been":[56],"invented":[57],"that":[58],"attained":[60],"great":[61],"success":[62],"in":[63,81,124,147,172,202,205],"constructing":[67],"a":[68,94,106,131,148],"binary":[69],"tree":[72,98,112,159,170],"efficiently":[74],"focusing":[75],"on":[76,187],"most":[78],"valuable":[79],"features":[80,83],"space.":[84],"However,":[85],"one":[86],"challenging":[87],"problem":[88],"associated":[89],"such":[91],"approaches":[92],"tradeoff":[95],"search":[99,171],"number":[102,108],"simulations.":[104],"In":[105,128,181],"limited":[107],"simulations,":[110],"might":[113],"not":[114],"meet":[115],"sufficient":[117],"depth,":[118],"thus":[119,165],"inducing":[120],"biasness":[121],"towards":[122],"randomness":[123],"subset":[126],"selection.":[127],"this":[129,182],"paper,":[130],"new":[132],"algorithm":[133],"for":[134,191],"proposed":[138],"where":[139],"multiple":[140],"trees":[143],"built":[145],"iteratively":[146],"recursive":[149],"fashion.":[150],"state":[152],"space":[153],"every":[155],"successor":[156],"less":[161],"than":[162],"its":[163],"predecessor,":[164],"increasing":[166],"impact":[168],"selecting":[173],"best":[174],"features,":[175],"keeping":[176],"MCTS":[178],"simulations":[179],"fixed.":[180],"study,":[183],"experiments":[184],"performed":[186],"16":[188],"benchmark":[189],"datasets":[190],"validation":[192],"purposes.":[193],"We":[194],"also":[195],"compare":[196],"performance":[198],"state-of-the-art":[200],"methods":[201],"literature":[203],"both":[204],"terms":[206],"ratio.":[214]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3090616938","counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2024-11-23T01:00:58.877917","created_date":"2020-10-08"}
