{"id":"https://openalex.org/W4387483445","doi":"https://doi.org/10.1088/2632-2153/ad020e","title":"Feature space reduction method for ultrahigh-dimensional, multiclass data: random forest-based multiround screening (RFMS)","display_name":"Feature space reduction method for ultrahigh-dimensional, multiclass data: random forest-based multiround screening (RFMS)","publication_year":2023,"publication_date":"2023-10-10","ids":{"openalex":"https://openalex.org/W4387483445","doi":"https://doi.org/10.1088/2632-2153/ad020e"},"language":"en","primary_location":{"id":"doi:10.1088/2632-2153/ad020e","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ad020e","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ad020e/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ad020e/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082858229","display_name":"Gergely Hancz\u00e1r","orcid":"https://orcid.org/0000-0002-0222-1400"},"institutions":[{"id":"https://openalex.org/I4210108416","display_name":"Insight Healthcare","ror":"https://ror.org/020swhw58","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210108416"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gergely Hancz\u00e1r","raw_affiliation_strings":["Cursor Insight Ltd., 20-22 Wenlock Road, London, N17GU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND"],"raw_orcid":"https://orcid.org/0000-0002-0222-1400","affiliations":[{"raw_affiliation_string":"Cursor Insight Ltd., 20-22 Wenlock Road, London, N17GU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND","institution_ids":["https://openalex.org/I4210108416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022170663","display_name":"Marcell Stippinger","orcid":"https://orcid.org/0000-0002-9954-8089"},"institutions":[{"id":"https://openalex.org/I4210126225","display_name":"HUN-REN Wigner Research Centre for Physics","ror":"https://ror.org/035dsb084","country_code":"HU","type":"facility","lineage":["https://openalex.org/I4210126225","https://openalex.org/I4387152226"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Marcell Stippinger","raw_affiliation_strings":["Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Mikl\u00f3s Street, Budapest, 1121, HUNGARY"],"raw_orcid":"https://orcid.org/0000-0002-9954-8089","affiliations":[{"raw_affiliation_string":"Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Mikl\u00f3s Street, Budapest, 1121, HUNGARY","institution_ids":["https://openalex.org/I4210126225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067973192","display_name":"D\u00e1vid Han\u00e1k","orcid":"https://orcid.org/0000-0003-0678-9885"},"institutions":[{"id":"https://openalex.org/I4210108416","display_name":"Insight Healthcare","ror":"https://ror.org/020swhw58","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210108416"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"D\u00e1vid Han\u00e1k","raw_affiliation_strings":["Cursor Insight Ltd., 20-22 Wenlock Road, London, N17GU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND"],"raw_orcid":"https://orcid.org/0000-0003-0678-9885","affiliations":[{"raw_affiliation_string":"Cursor Insight Ltd., 20-22 Wenlock Road, London, N17GU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND","institution_ids":["https://openalex.org/I4210108416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032557198","display_name":"Marcell T. Kurbucz","orcid":"https://orcid.org/0000-0002-0121-6781"},"institutions":[{"id":"https://openalex.org/I4210126225","display_name":"HUN-REN Wigner Research Centre for Physics","ror":"https://ror.org/035dsb084","country_code":"HU","type":"facility","lineage":["https://openalex.org/I4210126225","https://openalex.org/I4387152226"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Marcell T Kurbucz","raw_affiliation_strings":["Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Mikl\u00f3s Street, Budapest, Budapest, 1121, HUNGARY"],"raw_orcid":"https://orcid.org/0000-0002-0121-6781","affiliations":[{"raw_affiliation_string":"Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Mikl\u00f3s Street, Budapest, Budapest, 1121, HUNGARY","institution_ids":["https://openalex.org/I4210126225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068440545","display_name":"Oliv\u00e9r M\u00e1t\u00e9 T\u00f6rteli","orcid":"https://orcid.org/0000-0002-2148-9189"},"institutions":[{"id":"https://openalex.org/I4210108416","display_name":"Insight Healthcare","ror":"https://ror.org/020swhw58","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210108416"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Oliv\u00e9r M T\u00f6rteli","raw_affiliation_strings":["Cursor Insight Ltd., 20-22 Wenlock Road, London, N17GU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND"],"raw_orcid":"https://orcid.org/0000-0002-2148-9189","affiliations":[{"raw_affiliation_string":"Cursor Insight Ltd., 20-22 Wenlock Road, London, N17GU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND","institution_ids":["https://openalex.org/I4210108416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092031215","display_name":"\u00c1gnes Chripk\u00f3","orcid":"https://orcid.org/0000-0002-2863-5257"},"institutions":[{"id":"https://openalex.org/I4210108416","display_name":"Insight Healthcare","ror":"https://ror.org/020swhw58","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210108416"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"\u00c1gnes Chripk\u00f3","raw_affiliation_strings":["Cursor Insight Ltd., 20-22 Wenlock Road, London, N17GU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND"],"raw_orcid":"https://orcid.org/0000-0002-2863-5257","affiliations":[{"raw_affiliation_string":"Cursor Insight Ltd., 20-22 Wenlock Road, London, N17GU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND","institution_ids":["https://openalex.org/I4210108416"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056283197","display_name":"Zolt\u00e1n Somogyv\u00e1ri","orcid":"https://orcid.org/0000-0002-4385-3025"},"institutions":[{"id":"https://openalex.org/I4210126225","display_name":"HUN-REN Wigner Research Centre for Physics","ror":"https://ror.org/035dsb084","country_code":"HU","type":"facility","lineage":["https://openalex.org/I4210126225","https://openalex.org/I4387152226"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Zolt\u00e1n Somogyv\u00e1ri","raw_affiliation_strings":["Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Mikl\u00f3s Street, Budapest, 1121, HUNGARY"],"raw_orcid":"https://orcid.org/0000-0002-4385-3025","affiliations":[{"raw_affiliation_string":"Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Mikl\u00f3s Street, Budapest, 1121, HUNGARY","institution_ids":["https://openalex.org/I4210126225"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5022170663","https://openalex.org/A5032557198"],"corresponding_institution_ids":["https://openalex.org/I4210126225"],"apc_list":{"value":1600,"currency":"GBP","value_usd":1962},"apc_paid":{"value":1600,"currency":"GBP","value_usd":1962},"fwci":0.4254,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.62819495,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"4","issue":"4","first_page":"045012","last_page":"045012"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9987000226974487,"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":0.9987000226974487,"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/T10828","display_name":"Biometric Identification and Security","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10885","display_name":"Gene expression and cancer classification","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6471114158630371},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5886558890342712},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5616250038146973},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5338839888572693},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.5285469889640808},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5074679255485535},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.5001111030578613},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.49320000410079956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4836772382259369},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4731750786304474},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4538021683692932},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.41810351610183716},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3259837031364441},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2288486659526825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6471114158630371},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5886558890342712},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5616250038146973},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5338839888572693},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.5285469889640808},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5074679255485535},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.5001111030578613},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.49320000410079956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4836772382259369},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4731750786304474},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4538021683692932},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.41810351610183716},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3259837031364441},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2288486659526825},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1088/2632-2153/ad020e","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ad020e","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ad020e/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},{"id":"pmh:oai:unipub.lib.uni-corvinus.hu:9520","is_oa":true,"landing_page_url":null,"pdf_url":"https://unipub.lib.uni-corvinus.hu/9520/1/Hanczar_2023.pdf","source":{"id":"https://openalex.org/S4306400280","display_name":"Corvinus Research Archive (Corvinus University of Budapest)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I163245316","host_organization_name":"Corvinus University of Budapest","host_organization_lineage":["https://openalex.org/I163245316"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},{"id":"pmh:oai:doaj.org/article:b9e76c4766c04692a96738a53f464413","is_oa":true,"landing_page_url":"https://doaj.org/article/b9e76c4766c04692a96738a53f464413","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":"Machine Learning: Science and Technology, Vol 4, Iss 4, p 045012 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1088/2632-2153/ad020e","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ad020e","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ad020e/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2116091562","display_name":null,"funder_award_id":"K135837","funder_id":"https://openalex.org/F4320326762","funder_display_name":"Nemzeti Kutat\u00e1si Fejleszt\u00e9si \u00e9s Innov\u00e1ci\u00f3s Hivatal"},{"id":"https://openalex.org/G4676791794","display_name":null,"funder_award_id":"K135837","funder_id":"https://openalex.org/F4320321994","funder_display_name":"Hungarian Scientific Research Fund"},{"id":"https://openalex.org/G5706402246","display_name":null,"funder_award_id":"PD142593","funder_id":"https://openalex.org/F4320321994","funder_display_name":"Hungarian Scientific Research Fund"}],"funders":[{"id":"https://openalex.org/F4320321994","display_name":"Hungarian Scientific Research Fund","ror":"https://ror.org/00v349e63"},{"id":"https://openalex.org/F4320326762","display_name":"Nemzeti Kutat\u00e1si Fejleszt\u00e9si \u00e9s Innov\u00e1ci\u00f3s Hivatal","ror":null},{"id":"https://openalex.org/F4320336161","display_name":"Wigner Fizikai Kutat\u00f3k\u00f6zpont, Magyar Tudom\u00e1nyos Akad\u00e9mia","ror":"https://ror.org/035dsb084"},{"id":"https://openalex.org/F4320336675","display_name":"National Research, Development and Innovation Office","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387483445.pdf"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1910317596","https://openalex.org/W1967320885","https://openalex.org/W1994884074","https://openalex.org/W2049701820","https://openalex.org/W2060542593","https://openalex.org/W2071128523","https://openalex.org/W2078957532","https://openalex.org/W2093019316","https://openalex.org/W2111626380","https://openalex.org/W2122111042","https://openalex.org/W2133487567","https://openalex.org/W2154560360","https://openalex.org/W2183877827","https://openalex.org/W2209237188","https://openalex.org/W2294798173","https://openalex.org/W2333273157","https://openalex.org/W2519344800","https://openalex.org/W2528103328","https://openalex.org/W2554382158","https://openalex.org/W2560033242","https://openalex.org/W2591314755","https://openalex.org/W2763137833","https://openalex.org/W2781560769","https://openalex.org/W2911964244","https://openalex.org/W2942899367","https://openalex.org/W2944954104","https://openalex.org/W2951072626","https://openalex.org/W2997199851","https://openalex.org/W3003977585","https://openalex.org/W3084021543","https://openalex.org/W3103385725","https://openalex.org/W3120740533","https://openalex.org/W3122008423","https://openalex.org/W3125378075","https://openalex.org/W4229744185","https://openalex.org/W4281918598","https://openalex.org/W4297896226","https://openalex.org/W4327972620","https://openalex.org/W4360616010"],"related_works":["https://openalex.org/W2076845124","https://openalex.org/W2183964146","https://openalex.org/W2062586268","https://openalex.org/W4388745254","https://openalex.org/W2980082554","https://openalex.org/W2767419625","https://openalex.org/W2389704471","https://openalex.org/W1517228774","https://openalex.org/W2117019857","https://openalex.org/W1980222719"],"abstract_inverted_index":{"Abstract":[0],"In":[1,56],"recent":[2],"years,":[3],"several":[4],"screening":[5,69,155],"methods":[6,31],"have":[7],"been":[8],"published":[9],"for":[10],"ultrahigh-dimensional":[11],"data":[12,34,49],"that":[13,71],"contain":[14],"hundreds":[15],"of":[16,18,21,29,37,54,93,109],"thousands":[17,36],"features,":[19],"many":[20,160],"which":[22],"are":[23,100],"irrelevant":[24,119],"or":[25],"redundant.":[26],"However,":[27],"most":[28],"these":[30],"cannot":[32],"handle":[33],"with":[35,152],"classes.":[38],"Prediction":[39],"models":[40,99],"built":[41],"to":[42,102],"authenticate":[43],"users":[44],"based":[45,111],"on":[46,112,144,150],"multichannel":[47],"biometric":[48,134],"result":[50],"in":[51],"this":[52,57],"type":[53],"problem.":[55],"study,":[58],"we":[59],"present":[60],"a":[61,91,132],"novel":[62],"method":[63],"known":[64,138],"as":[65,139],"random":[66],"forest-based":[67],"multiround":[68],"(RFMS)":[70],"can":[72],"be":[73],"effectively":[74],"applied":[75],"under":[76],"such":[77],"circumstances.":[78],"The":[79],"proposed":[80],"algorithm":[81,116],"divides":[82],"the":[83,107,145,147],"feature":[84,127,135,154],"space":[85,136],"into":[86],"small":[87],"subsets":[88],"and":[89,106,121,125],"executes":[90],"series":[92],"partial":[94,98],"model":[95],"builds.":[96],"These":[97],"used":[101],"implement":[103],"tournament-based":[104],"sorting":[105],"selection":[108],"features":[110,120],"their":[113],"importance.":[114],"This":[115],"successfully":[117],"filters":[118],"also":[122],"discovers":[123],"binary":[124],"higher-order":[126],"interactions.":[128],"To":[129],"benchmark":[130],"RFMS,":[131],"synthetic":[133],"generator":[137],"BiometricBlender":[140],"is":[141,149],"employed.":[142],"Based":[143],"results,":[146],"RFMS":[148],"par":[151],"industry-standard":[153],"methods,":[156],"while":[157],"simultaneously":[158],"possessing":[159],"advantages":[161],"over":[162],"them.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2023-10-11T00:00:00"}