{"id":"https://openalex.org/W3015269186","doi":"https://doi.org/10.1109/access.2020.2986232","title":"Improved Feature Selection Model for Big Data Analytics","display_name":"Improved Feature Selection Model for Big Data Analytics","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3015269186","doi":"https://doi.org/10.1109/access.2020.2986232","mag":"3015269186"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2986232","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2986232","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09058715.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09058715.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047244353","display_name":"Ibrahim M. El\u2010Hasnony","orcid":"https://orcid.org/0000-0002-9489-3449"},"institutions":[{"id":"https://openalex.org/I159247623","display_name":"Mansoura University","ror":"https://ror.org/01k8vtd75","country_code":"EG","type":"education","lineage":["https://openalex.org/I159247623"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Ibrahim M. El-Hasnony","raw_affiliation_strings":["Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt"],"raw_orcid":"https://orcid.org/0000-0002-9489-3449","affiliations":[{"raw_affiliation_string":"Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt","institution_ids":["https://openalex.org/I159247623"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015669985","display_name":"Sherif Barakat","orcid":"https://orcid.org/0000-0003-4448-293X"},"institutions":[{"id":"https://openalex.org/I159247623","display_name":"Mansoura University","ror":"https://ror.org/01k8vtd75","country_code":"EG","type":"education","lineage":["https://openalex.org/I159247623"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Sherif I. Barakat","raw_affiliation_strings":["Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt","institution_ids":["https://openalex.org/I159247623"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000985289","display_name":"Mohamed Elhoseny","orcid":"https://orcid.org/0000-0001-6347-8368"},"institutions":[{"id":"https://openalex.org/I159247623","display_name":"Mansoura University","ror":"https://ror.org/01k8vtd75","country_code":"EG","type":"education","lineage":["https://openalex.org/I159247623"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Mohamed Elhoseny","raw_affiliation_strings":["Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt"],"raw_orcid":"https://orcid.org/0000-0001-6347-8368","affiliations":[{"raw_affiliation_string":"Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt","institution_ids":["https://openalex.org/I159247623"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021159901","display_name":"Reham R. Mostafa","orcid":"https://orcid.org/0000-0001-6917-7873"},"institutions":[{"id":"https://openalex.org/I159247623","display_name":"Mansoura University","ror":"https://ror.org/01k8vtd75","country_code":"EG","type":"education","lineage":["https://openalex.org/I159247623"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Reham R. Mostafa","raw_affiliation_strings":["Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt","institution_ids":["https://openalex.org/I159247623"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I159247623"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":16.688,"has_fulltext":true,"cited_by_count":170,"citation_normalized_percentile":{"value":0.99346206,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"8","issue":null,"first_page":"66989","last_page":"67004"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9988999962806702,"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":"Metaheuristic Optimization Algorithms Research","score":0.9988999962806702,"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":0.9921000003814697,"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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9905999898910522,"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":0.7207396030426025},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7003973722457886},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6411336660385132},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5820004343986511},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.5272161960601807},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5255019664764404},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49663811922073364},{"id":"https://openalex.org/keywords/local-optimum","display_name":"Local optimum","score":0.44601207971572876},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4390574097633362},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4271220564842224},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4253905415534973},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.42151811718940735},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4149152934551239},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30055126547813416},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13784489035606384}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7207396030426025},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7003973722457886},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6411336660385132},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5820004343986511},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.5272161960601807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5255019664764404},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49663811922073364},{"id":"https://openalex.org/C141934464","wikidata":"https://www.wikidata.org/wiki/Q3305386","display_name":"Local optimum","level":2,"score":0.44601207971572876},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4390574097633362},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4271220564842224},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4253905415534973},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.42151811718940735},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4149152934551239},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30055126547813416},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13784489035606384},{"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":2,"locations":[{"id":"doi:10.1109/access.2020.2986232","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2986232","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09058715.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7f1fecfba9c54fcaabac3d800b698715","is_oa":true,"landing_page_url":"https://doaj.org/article/7f1fecfba9c54fcaabac3d800b698715","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":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 66989-67004 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2986232","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2986232","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09058715.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3015269186.pdf","grobid_xml":"https://content.openalex.org/works/W3015269186.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1418108976","https://openalex.org/W1822352794","https://openalex.org/W2044371115","https://openalex.org/W2061438946","https://openalex.org/W2117533322","https://openalex.org/W2133077909","https://openalex.org/W2282752563","https://openalex.org/W2288385260","https://openalex.org/W2341338744","https://openalex.org/W2350552394","https://openalex.org/W2528103328","https://openalex.org/W2542691202","https://openalex.org/W2564781577","https://openalex.org/W2582636227","https://openalex.org/W2593487812","https://openalex.org/W2606926586","https://openalex.org/W2735994428","https://openalex.org/W2753434909","https://openalex.org/W2770884289","https://openalex.org/W2789477383","https://openalex.org/W2792304633","https://openalex.org/W2793851108","https://openalex.org/W2810786926","https://openalex.org/W2886555869","https://openalex.org/W2886793380","https://openalex.org/W2895771060","https://openalex.org/W2896052764","https://openalex.org/W2900503810","https://openalex.org/W2904861085","https://openalex.org/W2912293312","https://openalex.org/W2921646645","https://openalex.org/W2924610876","https://openalex.org/W2932046728","https://openalex.org/W2942137712","https://openalex.org/W2949161208","https://openalex.org/W2953484196","https://openalex.org/W2963042850","https://openalex.org/W2964307072","https://openalex.org/W2964358470","https://openalex.org/W2965837582","https://openalex.org/W2986805221","https://openalex.org/W3120740533","https://openalex.org/W6680096201","https://openalex.org/W6704354068"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4295122168","https://openalex.org/W3155717344","https://openalex.org/W1770458422","https://openalex.org/W3208659968","https://openalex.org/W4286891850"],"abstract_inverted_index":{"Although":[0],"there":[1],"are":[2,196,204],"many":[3],"attempts":[4],"to":[5,41,69,113,129,147,151,154,164,206,244,261],"build":[6],"an":[7],"optimal":[8,116],"model":[9,217],"for":[10,76,138,198,272],"feature":[11,61,92,158],"selection":[12,62,93],"in":[13,123,248],"Big":[14],"Data":[15],"applications,":[16],"the":[17,33,42,52,60,77,90,115,125,130,140,155,166,183,189,199,208,211,215,234,249,252],"complex":[18],"nature":[19],"of":[20,24,47,89,157,171,214,240],"processing":[21,270],"such":[22,181],"kind":[23],"data":[25,34,49],"makes":[26],"it":[27],"still":[28],"a":[29,65,85,144,148],"big":[30],"challenge.":[31],"Accordingly,":[32],"mining":[35],"process":[36,63],"may":[37],"be":[38,152],"obstructed":[39],"due":[40],"high":[43],"dimensionality":[44],"and":[45,56,97,178,188,201,210,213,226,246,251,266,280,284],"complexity":[46],"huge":[48],"sets.":[50],"For":[51],"most":[53],"informative":[54],"features":[55,79,222,231],"classification":[57,224],"accuracy":[58,257],"optimization,":[59],"constitutes":[64],"mandatory":[66],"pre-processing":[67],"phase":[68],"reduce":[70],"dataset":[71],"dimensionality.":[72],"The":[73,103,134,229,255,268],"exhaustive":[74],"search":[75,141],"relevant":[78],"is":[80,101,111,162,258],"time-consuming.":[81],"In":[82],"this":[83],"paper,":[84],"new":[86],"binary":[87,149],"variant":[88],"wrapper":[91],"grey":[94,190],"wolf":[95,191],"optimization":[96,100,186,192],"particle":[98,184],"swarm":[99,185],"proposed.":[102],"K-nearest":[104],"neighbor":[105],"classifier":[106],"with":[107,176,218],"Euclidean":[108],"separation":[109],"matrices":[110],"used":[112,163,197],"find":[114],"solutions.":[117],"A":[118,169],"tent":[119],"chaotic":[120],"map":[121],"helps":[122],"avoiding":[124],"algorithm":[126],"from":[127,143],"locked":[128],"local":[131],"optima":[132],"problem.":[133],"sigmoid":[135],"function":[136],"employed":[137],"converting":[139],"space":[142],"continuous":[145],"vector":[146],"one":[150],"suitable":[153],"problem":[156],"selection.":[159],"Cross-validation":[160],"K-fold":[161],"overcome":[165],"overfitting":[167],"issue.":[168],"variety":[170],"comparisons":[172],"have":[173],"been":[174],"made":[175],"well-known":[177],"common":[179],"algorithms,":[180],"as":[182,242],"algorithm,":[187],"algorithm.":[193],"Twenty":[194],"datasets":[195,236,274],"experiments,":[200],"statistical":[202],"analyses":[203],"conducted":[205],"approve":[207],"performance":[209],"effectiveness":[212],"proposed":[216],"measures":[219],"like":[220],"selected":[221],"ratio,":[223],"accuracy,":[225],"computation":[227],"time.":[228],"cumulative":[230],"picked":[232],"through":[233],"twenty":[235],"were":[237],"196":[238],"out":[239],"773,":[241],"opposed":[243],"393":[245],"336":[247],"GWO":[250,279],"PSO,":[253],"respectively.":[254,286],"overall":[256],"90%":[259],"relative":[260],"other":[262],"algorithms":[263],"'":[264],"81.6":[265],"86.8.":[267],"total":[269],"time":[271],"all":[273],"equals":[275],"184.3":[276],"seconds,":[277],"wherein":[278],"PSO":[281],"equal":[282],"272":[283],"245.6,":[285]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":32},{"year":2021,"cited_by_count":62},{"year":2020,"cited_by_count":13}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}