{"id":"https://openalex.org/W2913575720","doi":"https://doi.org/10.1093/llc/fqz003","title":"Comparing multiple categories of feature selection methods for text classification","display_name":"Comparing multiple categories of feature selection methods for text classification","publication_year":2019,"publication_date":"2019-01-09","ids":{"openalex":"https://openalex.org/W2913575720","doi":"https://doi.org/10.1093/llc/fqz003","mag":"2913575720"},"language":"en","primary_location":{"id":"doi:10.1093/llc/fqz003","is_oa":false,"landing_page_url":"https://doi.org/10.1093/llc/fqz003","pdf_url":null,"source":{"id":"https://openalex.org/S2734814886","display_name":"Digital Scholarship in the Humanities","issn_l":"2055-7671","issn":["2055-7671","2055-768X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Digital Scholarship in the Humanities","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/A5100611610","display_name":"Wanwan Zheng","orcid":"https://orcid.org/0000-0003-4689-6044"},"institutions":[{"id":"https://openalex.org/I133984924","display_name":"Doshisha University","ror":"https://ror.org/01fxdkm29","country_code":"JP","type":"education","lineage":["https://openalex.org/I133984924"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Wanwan Zheng","raw_affiliation_strings":["Graduate School of Culture and Information Science, Doshisha University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Culture and Information Science, Doshisha University, Japan","institution_ids":["https://openalex.org/I133984924"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046812287","display_name":"Mingzhe Jin","orcid":"https://orcid.org/0000-0002-6012-1379"},"institutions":[{"id":"https://openalex.org/I133984924","display_name":"Doshisha University","ror":"https://ror.org/01fxdkm29","country_code":"JP","type":"education","lineage":["https://openalex.org/I133984924"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mingzhe Jin","raw_affiliation_strings":["Graduate School of Culture and Information Science, Doshisha University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Culture and Information Science, Doshisha University, Japan","institution_ids":["https://openalex.org/I133984924"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100611610"],"corresponding_institution_ids":["https://openalex.org/I133984924"],"apc_list":{"value":2754,"currency":"GBP","value_usd":3632},"apc_paid":null,"fwci":0.7963,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.79234249,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9998000264167786,"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":0.9998000264167786,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9973999857902527,"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"}},{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9955000281333923,"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/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.778701901435852},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6934230923652649},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6597852110862732},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.656633734703064},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.539563000202179},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.526075541973114},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5085843801498413},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5011551380157471},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.493522047996521},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47539982199668884},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4641355872154236},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4276917576789856},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2011406123638153}],"concepts":[{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.778701901435852},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6934230923652649},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6597852110862732},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.656633734703064},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.539563000202179},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.526075541973114},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5085843801498413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5011551380157471},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.493522047996521},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47539982199668884},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4641355872154236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4276917576789856},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2011406123638153},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1093/llc/fqz003","is_oa":false,"landing_page_url":"https://doi.org/10.1093/llc/fqz003","pdf_url":null,"source":{"id":"https://openalex.org/S2734814886","display_name":"Digital Scholarship in the Humanities","issn_l":"2055-7671","issn":["2055-7671","2055-768X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Digital Scholarship in the Humanities","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G4805775314","display_name":"A Study of Stylistic Change in Japanese Based on Data Science and Modeling of its Structure","funder_award_id":"18K00627","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5928231991","display_name":"Emotions Driving Discourse and the Transformation of Fascism: Text Mining Analysis of German, Italian, French, and Japanese Sources","funder_award_id":"19KT0002","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W298402441","https://openalex.org/W1980229662","https://openalex.org/W1991713115","https://openalex.org/W2017399882","https://openalex.org/W2039824374","https://openalex.org/W2055002552","https://openalex.org/W2097176879","https://openalex.org/W2100235759","https://openalex.org/W2103333826","https://openalex.org/W2106525823","https://openalex.org/W2112043903","https://openalex.org/W2118020653","https://openalex.org/W2137573778","https://openalex.org/W2156665896","https://openalex.org/W2167101736","https://openalex.org/W2315654089","https://openalex.org/W2587657135","https://openalex.org/W2603996486","https://openalex.org/W2613637861","https://openalex.org/W2664924928","https://openalex.org/W2737362886","https://openalex.org/W2798307820","https://openalex.org/W2803393527","https://openalex.org/W2805162503","https://openalex.org/W2806931302","https://openalex.org/W2811177504","https://openalex.org/W2998216295","https://openalex.org/W2998768810","https://openalex.org/W3124666641","https://openalex.org/W4247490522","https://openalex.org/W4285719527","https://openalex.org/W6660207297","https://openalex.org/W6674382832","https://openalex.org/W6699535048","https://openalex.org/W6733635152","https://openalex.org/W6751798821","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W4382795578","https://openalex.org/W1431147547","https://openalex.org/W2355463328","https://openalex.org/W2402648945","https://openalex.org/W2053213469","https://openalex.org/W2055761197","https://openalex.org/W2771741613","https://openalex.org/W2057608111","https://openalex.org/W939486154","https://openalex.org/W2262622710"],"abstract_inverted_index":{"Selecting":[0],"effective":[1,78],"features":[2,29],"from":[3],"data":[4,14,39],"sets":[5],"is":[6,24,56,61,86,197],"a":[7,65],"particularly":[8],"important":[9],"part":[10],"in":[11],"text":[12],"classification,":[13],"mining,":[15],"pattern":[16],"recognition,":[17],"and":[18,34,58,123,127,131,173,183],"artificial":[19],"intelligence.":[20],"Feature":[21],"selection":[22],"(FS)":[23],"capable":[25],"of":[26,38,52,68,91,108,134,170,186],"excluding":[27],"irrelevant":[28],"for":[30,97],"the":[31,36,50,75,129,148,164,181,198,202],"classification":[32],"task":[33],"reducing":[35],"dimensionality":[37],"sets,":[40],"which":[41],"help":[42],"us":[43],"better":[44],"understand":[45],"data.":[46],"Through":[47],"FS":[48,69,92,109,117,139,151],"selection,":[49],"performance":[51,175],"machine":[53,161],"learning":[54],"techniques":[55],"improved,":[57],"computation":[59],"requirement":[60],"minimized.":[62],"Thus":[63],"far,":[64],"large":[66],"number":[67],"methods":[70,93,118,140,152,188],"have":[71,102],"been":[72,82],"proposed,":[73],"whereas":[74],"most":[76],"practically":[77],"one":[79],"has":[80],"not":[81],"found.":[83],"Although":[84],"it":[85],"conceivable":[87],"that":[88,194],"different":[89,95,121,168,174],"categories":[90,107,122],"follow":[94],"criteria":[96],"evaluating":[98,105,126],"variables,":[99],"rare":[100],"studies":[101],"focused":[103],"on":[104,125,201],"various":[106],"methods.":[110,136],"This":[111],"article":[112],"first":[113],"lists":[114],"thirteen":[115,138],"superior":[116],"under":[119],"five":[120,150],"focuses":[124],"comparing":[128],"effectiveness":[130,182],"general":[132,184],"versatility":[133,185],"these":[135,187],"The":[137,190],"were":[141,153,177],"ranked":[142],"using":[143],"rank":[144],"aggregation":[145],"method.":[146],"Subsequently,":[147],"best":[149,199],"elected":[154],"to":[155,179],"perform":[156],"multi-class":[157],"classifications.":[158],"Support":[159],"vector":[160],"served":[162],"as":[163],"classifier.":[165],"Different":[166],"languages,":[167],"numbers":[169],"selected":[171],"features,":[172],"measures":[176],"employed":[178],"measure":[180],"together.":[189],"analysis":[191],"results":[192],"suggest":[193],"Mahalanobis":[195],"distance":[196],"method":[200],"whole.":[203]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}