{"id":"https://openalex.org/W4240151859","doi":"https://doi.org/10.1504/ijdmmm.2017.10009449","title":"Hybrid feature selection methods for high-dimensional multi-class datasets","display_name":"Hybrid feature selection methods for high-dimensional multi-class datasets","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W4240151859","doi":"https://doi.org/10.1504/ijdmmm.2017.10009449"},"language":"en","primary_location":{"id":"doi:10.1504/ijdmmm.2017.10009449","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijdmmm.2017.10009449","pdf_url":null,"source":{"id":"https://openalex.org/S42848612","display_name":"International Journal of Data Mining Modelling and Management","issn_l":"1759-1163","issn":["1759-1163","1759-1171"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Mining, Modelling and Management","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/A5100605877","display_name":"John Wang","orcid":"https://orcid.org/0000-0002-8515-5014"},"institutions":[{"id":"https://openalex.org/I166088655","display_name":"Montclair State University","ror":"https://ror.org/01nxc2t48","country_code":"US","type":"education","lineage":["https://openalex.org/I166088655"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Wang","raw_affiliation_strings":["Department of Information Management and Business Analysis, Montclair State University, Montclair, NJ 07043, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Management and Business Analysis, Montclair State University, Montclair, NJ 07043, USA","institution_ids":["https://openalex.org/I166088655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018578583","display_name":"Amit Saxena","orcid":"https://orcid.org/0000-0003-2590-7234"},"institutions":[{"id":"https://openalex.org/I26285277","display_name":"Guru Ghasidas Vishwavidyalaya","ror":"https://ror.org/05bvxq496","country_code":"IN","type":"education","lineage":["https://openalex.org/I26285277"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amit Kumar Saxena","raw_affiliation_strings":["Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, 495009, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, 495009, India","institution_ids":["https://openalex.org/I26285277"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076065162","display_name":"Vimal Kumar Dubey","orcid":null},"institutions":[{"id":"https://openalex.org/I26285277","display_name":"Guru Ghasidas Vishwavidyalaya","ror":"https://ror.org/05bvxq496","country_code":"IN","type":"education","lineage":["https://openalex.org/I26285277"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vimal Kumar Dubey","raw_affiliation_strings":["Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, 495009, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, 495009, India","institution_ids":["https://openalex.org/I26285277"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0902,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.52899873,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"9","issue":"4","first_page":"315","last_page":"315"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9296000003814697,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9296000003814697,"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/feature-selection","display_name":"Feature selection","score":0.7613886594772339},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6167421936988831},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6109533905982971},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6007376909255981},{"id":"https://openalex.org/keywords/information-gain","display_name":"Information gain","score":0.5545687079429626},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5352424383163452},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5320500135421753},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48187121748924255},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4774132966995239},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4719516336917877},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27263084053993225}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7613886594772339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6167421936988831},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6109533905982971},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6007376909255981},{"id":"https://openalex.org/C2983203078","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Information gain","level":2,"score":0.5545687079429626},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5352424383163452},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5320500135421753},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48187121748924255},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4774132966995239},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4719516336917877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27263084053993225},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","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":1,"locations":[{"id":"doi:10.1504/ijdmmm.2017.10009449","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijdmmm.2017.10009449","pdf_url":null,"source":{"id":"https://openalex.org/S42848612","display_name":"International Journal of Data Mining Modelling and Management","issn_l":"1759-1163","issn":["1759-1163","1759-1171"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Mining, Modelling and Management","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2094189286","https://openalex.org/W4386564352","https://openalex.org/W2952668426","https://openalex.org/W2350358035","https://openalex.org/W4237536247","https://openalex.org/W2328484534","https://openalex.org/W16133775","https://openalex.org/W2465988918","https://openalex.org/W2367691850","https://openalex.org/W2360805930"],"abstract_inverted_index":{"Hybrid":[0],"methods":[1,23,178,212],"are":[2,25,56,69,103,148,161,179,213],"very":[3],"important":[4],"for":[5,83,93,172,218],"feature":[6,30,88,94,112,157],"selection":[7,85,95,113],"in":[8,108],"case":[9],"of":[10,13,28,45,86,99,118,146,152],"the":[11,26,54,60,72,84,116,137,174],"classification":[12,175],"high-dimensional":[14,183],"datasets.":[15,185],"In":[16,51,127],"this":[17,128],"paper,":[18],"we":[19],"proposed":[20,41,211],"two":[21,97],"hybrid":[22],"which":[24,102],"combination":[27],"filter-based":[29],"selection,":[31],"genetic":[32,49],"algorithm,":[33],"and":[34,48,63,106,121,139,163,198],"sequential":[35,122,167],"random":[36,123],"search":[37,168],"methods.":[38,220],"The":[39,110,207],"first":[40,193],"method":[42,169],"is":[43,81,91,115,133,170,196,205],"hybridisation":[44,117],"information":[46,61,119,131],"gain":[47,62,120,132],"algorithm.":[50],"this,":[52],"first,":[53],"features":[55,68,80,138,147],"ranked":[57,73,144],"based":[58],"on":[59,164,190,199],"then":[64],"a":[65,140],"user":[66,141],"defined":[67,142],"selected":[70,79,158],"from":[71],"features.":[74],"Genetic":[75],"algorithm":[76],"with":[77,96,215],"these":[78],"applied":[82,92,171,180],"optimal":[87],"subset.":[89],"It":[90],"types":[98],"fitness":[100],"functions":[101],"single":[104],"objective":[105],"multi-objective":[107],"nature.":[109],"second":[111,202],"model":[114],"K-nearest":[124],"neighbour":[125],"(SRKNN).":[126],"method,":[129],"again":[130],"used":[134],"to":[135,181],"rank":[136],"top":[143],"number":[145],"selected.":[149],"A":[150],"set":[151],"binary":[153],"population":[154,166],"(having":[155],"all":[156],"by":[159,210],"users)":[160],"generated":[162],"each":[165],"maximising":[173],"accuracy.":[176],"These":[177],"21":[182],"multi-class":[184],"Obtained":[186],"results":[187,208,216],"show":[188],"that":[189],"some":[191,200],"datasets":[192,201],"method's":[194,203],"performance":[195,204],"good":[197],"good.":[206],"obtained":[209],"compared":[214],"registered":[217],"other":[219]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}