{"id":"https://openalex.org/W2257550357","doi":"https://doi.org/10.1145/2976744","title":"Scalable and Accurate Online Feature Selection for Big Data","display_name":"Scalable and Accurate Online Feature Selection for Big Data","publication_year":2016,"publication_date":"2016-12-03","ids":{"openalex":"https://openalex.org/W2257550357","doi":"https://doi.org/10.1145/2976744","mag":"2257550357"},"language":"en","primary_location":{"id":"doi:10.1145/2976744","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2976744","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","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/A5100719462","display_name":"Kui Yu","orcid":"https://orcid.org/0000-0003-2442-4572"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Kui Yu","raw_affiliation_strings":["Simon Fraser University, BC, Canada"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080738591","display_name":"Xindong Wu","orcid":"https://orcid.org/0000-0003-2396-1704"},"institutions":[{"id":"https://openalex.org/I79516672","display_name":"University of Louisiana at Lafayette","ror":"https://ror.org/01x8rc503","country_code":"US","type":"education","lineage":["https://openalex.org/I2799628689","https://openalex.org/I79516672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xindong Wu","raw_affiliation_strings":["University of Louisiana at Lafayette, LA, USA"],"affiliations":[{"raw_affiliation_string":"University of Louisiana at Lafayette, LA, USA","institution_ids":["https://openalex.org/I79516672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088432110","display_name":"Wei Ding","orcid":"https://orcid.org/0000-0002-3383-551X"},"institutions":[{"id":"https://openalex.org/I33434090","display_name":"University of Massachusetts Boston","ror":"https://ror.org/04ydmy275","country_code":"US","type":"education","lineage":["https://openalex.org/I33434090"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Ding","raw_affiliation_strings":["University of Massachusetts Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Boston, MA, USA","institution_ids":["https://openalex.org/I33434090"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062247330","display_name":"Jian Pei","orcid":"https://orcid.org/0000-0002-2200-8711"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jian Pei","raw_affiliation_strings":["Simon Fraser University, BC, Canada"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100719462"],"corresponding_institution_ids":["https://openalex.org/I18014758"],"apc_list":null,"apc_paid":null,"fwci":6.9479,"has_fulltext":false,"cited_by_count":143,"citation_normalized_percentile":{"value":0.98074189,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"11","issue":"2","first_page":"1","last_page":"39"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9954000115394592,"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":0.9954000115394592,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9868000149726868,"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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9843999743461609,"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/feature-selection","display_name":"Feature selection","score":0.7867276668548584},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.7847285270690918},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.7199192643165588},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6564191579818726},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6514934301376343},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6297045350074768},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6080064177513123},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5970124006271362},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.555365264415741},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5084766149520874},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43969565629959106},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.43766412138938904},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4336523413658142},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43047839403152466},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3335840106010437},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0649225115776062}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7867276668548584},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7847285270690918},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.7199192643165588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6564191579818726},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6514934301376343},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6297045350074768},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6080064177513123},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5970124006271362},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.555365264415741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5084766149520874},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43969565629959106},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.43766412138938904},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4336523413658142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43047839403152466},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3335840106010437},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0649225115776062},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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:10.1145/2976744","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2976744","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5799999833106995}],"awards":[{"id":"https://openalex.org/G799543479","display_name":null,"funder_award_id":"61229301 and 61305064","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W149574399","https://openalex.org/W167115076","https://openalex.org/W179492584","https://openalex.org/W1516560083","https://openalex.org/W1829443747","https://openalex.org/W1849729440","https://openalex.org/W1887132526","https://openalex.org/W1949281989","https://openalex.org/W1965555277","https://openalex.org/W1979622093","https://openalex.org/W1988022329","https://openalex.org/W2000653282","https://openalex.org/W2001502947","https://openalex.org/W2002420020","https://openalex.org/W2013374740","https://openalex.org/W2017337590","https://openalex.org/W2032909675","https://openalex.org/W2038894244","https://openalex.org/W2040263621","https://openalex.org/W2052164429","https://openalex.org/W2058971980","https://openalex.org/W2068696370","https://openalex.org/W2074839723","https://openalex.org/W2097839764","https://openalex.org/W2102831150","https://openalex.org/W2103333826","https://openalex.org/W2112737239","https://openalex.org/W2127658298","https://openalex.org/W2131101925","https://openalex.org/W2132379769","https://openalex.org/W2133091666","https://openalex.org/W2135046866","https://openalex.org/W2136051823","https://openalex.org/W2138019504","https://openalex.org/W2145511633","https://openalex.org/W2153338628","https://openalex.org/W2154053567","https://openalex.org/W2155648952","https://openalex.org/W2156504490","https://openalex.org/W2156571267","https://openalex.org/W2165190832","https://openalex.org/W2169038408","https://openalex.org/W2214080290","https://openalex.org/W2247585390","https://openalex.org/W2615497679","https://openalex.org/W2951528775","https://openalex.org/W2963647909","https://openalex.org/W2997546679","https://openalex.org/W2998216295","https://openalex.org/W3005526988","https://openalex.org/W3006449229","https://openalex.org/W4285719527","https://openalex.org/W6678992168"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W4391160746","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W20047544","https://openalex.org/W3089231081"],"abstract_inverted_index":{"Feature":[0],"selection":[1,42,70,188],"is":[2,25,142],"important":[3],"in":[4,18,28,44,55,71,99],"many":[5,19],"big":[6,15,20,34],"data":[7,21,35],"applications.":[8],"Two":[9],"critical":[10],"challenges":[11],"closely":[12],"associate":[13],"with":[14,106],"data.":[16],"First,":[17],"applications,":[22],"the":[23,81,115,145,185],"dimensionality":[24,179],"extremely":[26,177],"high,":[27],"millions,":[29],"and":[30,64,92,118,150,170,180],"keeps":[31],"growing.":[32],"Second,":[33],"applications":[36],"call":[37],"for":[38,68,125],"highly":[39],"scalable":[40,173],"feature":[41,51,69,128,139,187],"algorithms":[43],"an":[45,100],"online":[46,101,126,134],"manner":[47],"such":[48],"that":[49,109,141,165],"each":[50],"can":[52,133],"be":[53],"processed":[54],"a":[56,62,75,94,121,136,158],"sequential":[57],"scan.":[58],"We":[59],"present":[60],"SAOLA,":[61],"<underline>S</underline>calable":[63],"<underline>A</underline>ccurate":[65],"<underline>O</underline>n<underline>L</underline>ine":[66],"<underline>A</underline>pproach":[67],"this":[72],"paper.":[73],"With":[74],"theoretical":[76],"analysis":[77],"on":[78,174],"bounds":[79],"of":[80,138,147,160,176],"pairwise":[82,89],"correlations":[83],"between":[84],"features,":[85],"SAOLA":[86,116,169],"employs":[87],"novel":[88],"comparison":[90],"techniques":[91],"maintains":[93],"parsimonious":[95],"model":[96],"over":[97,184],"time":[98],"manner.":[102],"Furthermore,":[103],"to":[104],"deal":[105],"upcoming":[107],"features":[108,152],"arrive":[110],"by":[111],"groups,":[112],"we":[113],"extend":[114],"algorithm,":[117],"then":[119],"propose":[120],"new":[122],"group-SAOLA":[123,131],"algorithm":[124,132],"group":[127],"selection.":[129],"The":[130],"maintain":[135],"set":[137],"groups":[140,149],"sparse":[143],"at":[144],"levels":[146],"both":[148],"individual":[151],"simultaneously.":[153],"An":[154],"empirical":[155],"study":[156],"using":[157],"series":[159],"benchmark":[161],"real":[162],"datasets":[163,175],"shows":[164],"our":[166],"two":[167],"algorithms,":[168],"group-SAOLA,":[171],"are":[172],"high":[178],"have":[181],"superior":[182],"performance":[183],"state-of-the-art":[186],"methods.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}