{"id":"https://openalex.org/W2788592483","doi":"https://doi.org/10.1609/aaai.v32i1.11820","title":"A Framework for Multistream Regression With Direct Density Ratio Estimation","display_name":"A Framework for Multistream Regression With Direct Density Ratio Estimation","publication_year":2018,"publication_date":"2018-04-29","ids":{"openalex":"https://openalex.org/W2788592483","doi":"https://doi.org/10.1609/aaai.v32i1.11820","mag":"2788592483"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v32i1.11820","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v32i1.11820","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/11820/11679","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/11820/11679","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016050605","display_name":"Ahsanul Haque","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ahsanul Haque","raw_affiliation_strings":["University of Texas at Dallas"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047994911","display_name":"Hemeng Tao","orcid":"https://orcid.org/0000-0002-3763-2269"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hemeng Tao","raw_affiliation_strings":["University of Texas at Dallas"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056179290","display_name":"Swarup Chandra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swarup Chandra","raw_affiliation_strings":["University of Texas at Dallas"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101672754","display_name":"Jie Liu","orcid":"https://orcid.org/0000-0002-2504-7492"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Liu","raw_affiliation_strings":["University of Texas at Dallas"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005002693","display_name":"Latifur Khan","orcid":"https://orcid.org/0000-0002-9300-1576"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Latifur Khan","raw_affiliation_strings":["University of Computer Science at Dallas"],"affiliations":[{"raw_affiliation_string":"University of Computer Science at Dallas","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5016050605"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5475,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.75630915,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"32","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9937000274658203,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9785000085830688,"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":0.6421819925308228},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5671932101249695},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5570117235183716},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.5489932298660278},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.500577449798584},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.478960782289505},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47705593705177307},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4007743299007416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32625341415405273},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.262958288192749},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1949908435344696}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6421819925308228},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5671932101249695},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5570117235183716},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.5489932298660278},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.500577449798584},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.478960782289505},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47705593705177307},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4007743299007416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32625341415405273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.262958288192749},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1949908435344696},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v32i1.11820","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v32i1.11820","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/11820/11679","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v32i1.11820","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v32i1.11820","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/11820/11679","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1191231006","display_name":null,"funder_award_id":"1737978","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1523888516","display_name":null,"funder_award_id":"FA9550-","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G519725923","display_name":null,"funder_award_id":"FA9550-14-1-017","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G5641167023","display_name":null,"funder_award_id":"DMS-1737978","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5809100787","display_name":null,"funder_award_id":"FA9550","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G5921281487","display_name":null,"funder_award_id":"number","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8985203359","display_name":null,"funder_award_id":"FA9550-14-1-0173","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2788592483.pdf","grobid_xml":"https://content.openalex.org/works/W2788592483.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W97587332","https://openalex.org/W133948627","https://openalex.org/W1520211939","https://openalex.org/W1585854823","https://openalex.org/W1834088915","https://openalex.org/W1962414156","https://openalex.org/W1993521476","https://openalex.org/W2028563099","https://openalex.org/W2032536435","https://openalex.org/W2062291443","https://openalex.org/W2075787792","https://openalex.org/W2099419573","https://openalex.org/W2103851188","https://openalex.org/W2112483442","https://openalex.org/W2150450267","https://openalex.org/W2150621701","https://openalex.org/W2413199960","https://openalex.org/W2434851943","https://openalex.org/W2470412537","https://openalex.org/W2532640750","https://openalex.org/W3120740533","https://openalex.org/W6603990131","https://openalex.org/W6631185847","https://openalex.org/W6635179022","https://openalex.org/W6638731126","https://openalex.org/W6676840641","https://openalex.org/W6680187362","https://openalex.org/W6682479753","https://openalex.org/W6715359748"],"related_works":["https://openalex.org/W4389449520","https://openalex.org/W127192698","https://openalex.org/W2570600173","https://openalex.org/W2893008024","https://openalex.org/W2743735673","https://openalex.org/W2360131081","https://openalex.org/W2985941356","https://openalex.org/W4361801939","https://openalex.org/W2802243998","https://openalex.org/W1521014365"],"abstract_inverted_index":{"Regression":[0],"over":[1,16,24,133,258],"a":[2,19,25,35,41,137,191,208,237,261],"stream":[3,27,162,215,228],"of":[4,52,169,206,270],"data":[5,11,26,31,69,73,87,134,147,165,180,193,222,251,283],"is":[6,28,46,90,172],"challenging":[7],"due":[8,65],"to":[9,49,66,107,152,197,203,216,295],"unbounded":[10],"size":[12],"and":[13,156,189,247,274,281],"non-stationary":[14,146],"distribution":[15,70,194,245,252],"time.":[17],"Typically,":[18],"traditional":[20],"supervised":[21],"regression":[22,131,241,298],"model":[23,45,60,77,111,123,209],"trained":[29,210],"on":[30,211,225,278],"instances":[32,106,166,181,223],"occurring":[33,224],"within":[34],"short":[36],"time":[37,259],"period":[38],"by":[39,254,291],"assuming":[40],"stationary":[42],"distribution.":[43],"This":[44],"later":[47],"used":[48,109],"predict":[50,217],"value":[51,83,168,185,220],"response-variable":[53],"in":[54,63,68,92,136,221,250],"future":[55],"instances.":[56,74],"Over":[57],"time,":[58],"the":[59,76,122,130,154,157,187,198,204,212,218,226,255,267,271,292],"may":[61,115],"degrade":[62],"performance":[64,290],"changes":[67],"among":[71],"incoming":[72],"Updating":[75],"for":[78,84,110,186,239],"change":[79,249],"adaptation":[80],"requires":[81],"true":[82],"every":[85],"recent":[86,98],"instances,":[88],"which":[89,149],"scarce":[91],"practice.":[93],"To":[94],"overcome":[95],"this":[96,114,126,233],"issue,":[97],"studies":[99],"have":[100],"employed":[101],"techniques":[102],"that":[103,119,242],"sample":[104],"fewer":[105],"be":[108],"retraining.":[112],"Yet,":[113],"introduce":[116],"sampling":[117],"bias":[118,246],"adversely":[120],"affects":[121],"performance.":[124],"In":[125,232],"paper,":[127,234],"we":[128,235],"study":[129],"problem":[132,205],"streams":[135,257],"novel":[138],"setting.":[139],"We":[140,201,265],"consider":[141],"two":[142,256],"independent,":[143],"yet":[144],"related,":[145],"streams,":[148],"are":[150],"referred":[151],"as":[153,229],"source":[155,175,214],"target":[158,161,199,227],"stream.":[159,200],"The":[160,174],"continuously":[163,178],"generates":[164,179],"whose":[167],"response":[170],"variable":[171],"unknown.":[173],"stream,":[176],"however,":[177],"along":[182],"with":[183,195],"corresponding":[184],"response-variable,":[188],"has":[190],"biased":[192,213],"respect":[196],"refer":[202],"using":[207,260],"response-variable\u2019s":[219],"Multistream":[230],"Regression.":[231],"describe":[236],"framework":[238,293],"multistream":[240],"simultaneously":[243],"overcomes":[244],"detects":[248],"represented":[253],"Gaussian":[262],"kernel":[263],"model.":[264],"analyze":[266],"theoretical":[268],"properties":[269],"proposed":[272],"approach":[273],"empirically":[275],"evaluate":[276],"it":[277],"both":[279],"real-world":[280],"synthetic":[282],"sets.":[284],"Importantly,":[285],"our":[286],"results":[287],"indicate":[288],"superior":[289],"compared":[294],"other":[296],"baseline":[297],"methods.":[299]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
