{"id":"https://openalex.org/W3108839440","doi":"https://doi.org/10.3390/bdcc4040037","title":"eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning","display_name":"eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning","publication_year":2020,"publication_date":"2020-11-28","ids":{"openalex":"https://openalex.org/W3108839440","doi":"https://doi.org/10.3390/bdcc4040037","mag":"3108839440"},"language":"en","primary_location":{"id":"doi:10.3390/bdcc4040037","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc4040037","pdf_url":"https://www.mdpi.com/2504-2289/4/4/37/pdf?version=1607060967","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-2289/4/4/37/pdf?version=1607060967","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078971072","display_name":"Khaled Fawagreh","orcid":"https://orcid.org/0000-0001-7225-0373"},"institutions":[{"id":"https://openalex.org/I138564716","display_name":"Prince Mohammad bin Fahd University","ror":"https://ror.org/03d64na34","country_code":"SA","type":"education","lineage":["https://openalex.org/I138564716"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Khaled Fawagreh","raw_affiliation_strings":["Department of Information Technology, Prince Mohammad Bin Fahd University, Dhahran 34754, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0001-7225-0373","affiliations":[{"raw_affiliation_string":"Department of Information Technology, Prince Mohammad Bin Fahd University, Dhahran 34754, Saudi Arabia","institution_ids":["https://openalex.org/I138564716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016157034","display_name":"Mohamed Medhat Gaber","orcid":"https://orcid.org/0000-0003-0339-4474"},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]},{"id":"https://openalex.org/I4400009028","display_name":"Galala University","ror":"https://ror.org/04x3ne739","country_code":null,"type":"education","lineage":["https://openalex.org/I4400009028"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Mohamed Medhat Gaber","raw_affiliation_strings":["Faculty of Computer Science and Engineering, Galala University, Attaka 43511, Egypt","School of Computing and Digital Technology, Birmingham City University, Birmingham B5 5JU, UK"],"raw_orcid":"https://orcid.org/0000-0003-0339-4474","affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Engineering, Galala University, Attaka 43511, Egypt","institution_ids":["https://openalex.org/I4400009028"]},{"raw_affiliation_string":"School of Computing and Digital Technology, Birmingham City University, Birmingham B5 5JU, UK","institution_ids":["https://openalex.org/I12870472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5016157034"],"corresponding_institution_ids":["https://openalex.org/I12870472","https://openalex.org/I4400009028"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.8154,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.798419,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"4","issue":"4","first_page":"37","last_page":"37"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9919999837875366,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9919999837875366,"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":"Evolutionary Algorithms and Applications","score":0.9724000096321106,"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/random-forest","display_name":"Random forest","score":0.8340756893157959},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.675692081451416},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6643767952919006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5416089296340942},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5279368758201599},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5184771418571472},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.511062741279602},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.48257318139076233},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.4806974530220032},{"id":"https://openalex.org/keywords/replicator-equation","display_name":"Replicator equation","score":0.4778832495212555},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4726768136024475},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.45314866304397583},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44362324476242065},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.43899163603782654},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.4248036742210388},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1809459626674652}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8340756893157959},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675692081451416},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6643767952919006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5416089296340942},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5279368758201599},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5184771418571472},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.511062741279602},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.48257318139076233},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.4806974530220032},{"id":"https://openalex.org/C50318809","wikidata":"https://www.wikidata.org/wiki/Q2145081","display_name":"Replicator equation","level":3,"score":0.4778832495212555},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4726768136024475},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.45314866304397583},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44362324476242065},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.43899163603782654},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.4248036742210388},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1809459626674652},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"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/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/bdcc4040037","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc4040037","pdf_url":"https://www.mdpi.com/2504-2289/4/4/37/pdf?version=1607060967","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e74d84f0b2574a16bce3da5caf4066f0","is_oa":true,"landing_page_url":"https://doaj.org/article/e74d84f0b2574a16bce3da5caf4066f0","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing, Vol 4, Iss 4, p 37 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-2289/4/4/37/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/bdcc4040037","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing; Volume 4; Issue 4; Pages: 37","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/bdcc4040037","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc4040037","pdf_url":"https://www.mdpi.com/2504-2289/4/4/37/pdf?version=1607060967","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"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,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3108839440.pdf","grobid_xml":"https://content.openalex.org/works/W3108839440.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W119626302","https://openalex.org/W1930624869","https://openalex.org/W1978229150","https://openalex.org/W2028798910","https://openalex.org/W2036083037","https://openalex.org/W2039263403","https://openalex.org/W2048697945","https://openalex.org/W2096579989","https://openalex.org/W2097141283","https://openalex.org/W2113242816","https://openalex.org/W2115629999","https://openalex.org/W2120240539","https://openalex.org/W2122892819","https://openalex.org/W2131416952","https://openalex.org/W2149206465","https://openalex.org/W2167055186","https://openalex.org/W2384329314","https://openalex.org/W2405151012","https://openalex.org/W2624452117","https://openalex.org/W2777105798","https://openalex.org/W2785373436","https://openalex.org/W2807747595","https://openalex.org/W2808358301","https://openalex.org/W2885104125","https://openalex.org/W2887924068","https://openalex.org/W2888050488","https://openalex.org/W2889841235","https://openalex.org/W2890770310","https://openalex.org/W2898535022","https://openalex.org/W2900386913","https://openalex.org/W2911964244","https://openalex.org/W2915213244","https://openalex.org/W2933013505","https://openalex.org/W2946696230","https://openalex.org/W2960390817","https://openalex.org/W2985765140","https://openalex.org/W3000675994","https://openalex.org/W3102745896","https://openalex.org/W3120740533","https://openalex.org/W4211095127","https://openalex.org/W4212883601","https://openalex.org/W4241399673","https://openalex.org/W6640462745","https://openalex.org/W6682163256","https://openalex.org/W6788247690","https://openalex.org/W6828608125"],"related_works":["https://openalex.org/W2373300491","https://openalex.org/W2492651500","https://openalex.org/W1615974028","https://openalex.org/W1212596013","https://openalex.org/W2378744544","https://openalex.org/W2594301978","https://openalex.org/W2379704676","https://openalex.org/W4206442282","https://openalex.org/W2384505857","https://openalex.org/W1566212037"],"abstract_inverted_index":{"To":[0],"make":[1],"healthcare":[2,38,71,81,90,99,132,151],"available":[3],"and":[4,42,50,64,120,139,148,184,188,192,245,261],"easily":[5],"accessible,":[6],"the":[7,14,17,23,57,62,78,124,127,160,200,224,229,248],"Internet":[8],"of":[9,19,25,56,61,67,80,126,137,174,203,210,218,226,247],"Things":[10],"(IoT),":[11],"which":[12,153],"paved":[13],"way":[15],"to":[16,46,102,122,144,167,199,223],"construction":[18],"smart":[20,27,37,70,150],"cities,":[21],"marked":[22],"birth":[24],"many":[26],"applications":[28,39,72],"in":[29,118,135,142,177,228],"numerous":[30],"areas,":[31],"including":[32],"healthcare.":[33],"As":[34],"a":[35,169,208],"result,":[36],"have":[40,155,214,251],"been":[41,252],"are":[43,74,182,239],"being":[44],"developed":[45],"provide,":[47],"using":[48,195,236],"mobile":[49],"electronic":[51],"technology,":[52],"higher":[53],"diagnosis":[54],"quality":[55,66],"diseases,":[58],"better":[59],"treatment":[60],"patients,":[63],"improved":[65],"lives.":[68],"Since":[69],"that":[73,220,238],"mainly":[75],"concerned":[76],"with":[77],"prediction":[79],"data":[82,91,100],"(like":[83],"diseases":[84],"for":[85,96],"example)":[86],"rely":[87],"on":[88,131,258],"predictive":[89,98,201],"analytics,":[92],"it":[93],"is":[94,221,234],"imperative":[95],"such":[97],"analytics":[101],"be":[103],"as":[104,106],"accurate":[105],"possible.":[107],"In":[108],"this":[109],"paper,":[110],"we":[111,154],"will":[112],"exploit":[113],"supervised":[114],"machine":[115],"learning":[116],"methods":[117],"classification":[119,260],"regression":[121,263],"improve":[123],"performance":[125],"traditional":[128],"Random":[129,170,180],"Forest":[130,171,181],"datasets,":[133],"both":[134],"terms":[136],"accuracy":[138,202,246],"classification/regression":[140],"speed,":[141],"order":[143],"produce":[145],"an":[146,178,215,255],"effective":[147],"efficient":[149],"application,":[152],"termed":[156],"eGAP.":[157],"eGAP":[158],"uses":[159],"evolutionary":[161],"game":[162],"theoretic":[163],"approach":[164],"replicator":[165,196],"dynamics":[166],"evolve":[168],"ensemble.":[172],"Trees":[173],"high":[175],"resemblance":[176],"initial":[179,216],"clustered,":[183],"then":[185],"clusters":[186,213],"grow":[187],"shrink":[189],"by":[190,207,254],"adding":[191],"removing":[193],"trees":[194,219,227,237],"dynamics,":[197],"according":[198],"each":[204],"subforest":[205],"represented":[206],"cluster":[209],"trees.":[211],"All":[212],"number":[217,225],"equal":[222],"smallest":[230],"cluster.":[231],"Cluster":[232],"growth":[233],"performed":[235],"not":[240],"initially":[241],"sampled.":[242],"The":[243],"speed":[244],"proposed":[249],"method":[250],"demonstrated":[253],"experimental":[256],"study":[257],"10":[259,262],"medical":[264],"datasets.":[265]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}