{"id":"https://openalex.org/W4362723906","doi":"https://doi.org/10.1080/08839514.2023.2198846","title":"Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams","display_name":"Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams","publication_year":2023,"publication_date":"2023-04-09","ids":{"openalex":"https://openalex.org/W4362723906","doi":"https://doi.org/10.1080/08839514.2023.2198846"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2023.2198846","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2023.2198846","pdf_url":"https://www.tandfonline.com/doi/epdf/10.1080/08839514.2023.2198846?needAccess=true&role=button","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.tandfonline.com/doi/epdf/10.1080/08839514.2023.2198846?needAccess=true&role=button","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016561889","display_name":"Jonathan Jakob","orcid":null},"institutions":[{"id":"https://openalex.org/I20121455","display_name":"Bielefeld University","ror":"https://ror.org/02hpadn98","country_code":"DE","type":"education","lineage":["https://openalex.org/I20121455"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Jonathan Jakob","raw_affiliation_strings":["Technical Faculty, Bielefeld University, Bielefeld, Germany","Technical Faculty, Bielefeld University, Bielefeld, Germany;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical Faculty, Bielefeld University, Bielefeld, Germany","institution_ids":["https://openalex.org/I20121455"]},{"raw_affiliation_string":"Technical Faculty, Bielefeld University, Bielefeld, Germany;","institution_ids":["https://openalex.org/I20121455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000081339","display_name":"Andr\u00e9 Artelt","orcid":"https://orcid.org/0000-0002-2426-3126"},"institutions":[{"id":"https://openalex.org/I20121455","display_name":"Bielefeld University","ror":"https://ror.org/02hpadn98","country_code":"DE","type":"education","lineage":["https://openalex.org/I20121455"]},{"id":"https://openalex.org/I34771391","display_name":"University of Cyprus","ror":"https://ror.org/02qjrjx09","country_code":"CY","type":"education","lineage":["https://openalex.org/I34771391"]}],"countries":["CY","DE"],"is_corresponding":false,"raw_author_name":"Andr\u00e9 Artelt","raw_affiliation_strings":["Department of Computer Science, University of Cyprus, Nicosia, Cyprus","Technical Faculty, Bielefeld University, Bielefeld, Germany","Department of Computer Science, University of Cyprus, Nicosia, Cyprus;","Technical Faculty, Bielefeld University, Bielefeld, Germany;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Cyprus, Nicosia, Cyprus","institution_ids":["https://openalex.org/I34771391"]},{"raw_affiliation_string":"Technical Faculty, Bielefeld University, Bielefeld, Germany","institution_ids":["https://openalex.org/I20121455"]},{"raw_affiliation_string":"Department of Computer Science, University of Cyprus, Nicosia, Cyprus;","institution_ids":["https://openalex.org/I34771391"]},{"raw_affiliation_string":"Technical Faculty, Bielefeld University, Bielefeld, Germany;","institution_ids":["https://openalex.org/I20121455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084633592","display_name":"Martina Hasenj\u00e4ger","orcid":"https://orcid.org/0000-0001-8438-5865"},"institutions":[{"id":"https://openalex.org/I4210112253","display_name":"Honda (Germany)","ror":"https://ror.org/022c1xk47","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210112253"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martina Hasenj\u00e4ger","raw_affiliation_strings":["Learning and Personalization Group, Honda Research Institute, Offenbach, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Learning and Personalization Group, Honda Research Institute, Offenbach, Germany","institution_ids":["https://openalex.org/I4210112253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091180862","display_name":"Barbara Hammer","orcid":"https://orcid.org/0000-0002-0935-5591"},"institutions":[{"id":"https://openalex.org/I20121455","display_name":"Bielefeld University","ror":"https://ror.org/02hpadn98","country_code":"DE","type":"education","lineage":["https://openalex.org/I20121455"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Barbara Hammer","raw_affiliation_strings":["Technical Faculty, Bielefeld University, Bielefeld, Germany","Technical Faculty, Bielefeld University, Bielefeld, Germany;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical Faculty, Bielefeld University, Bielefeld, Germany","institution_ids":["https://openalex.org/I20121455"]},{"raw_affiliation_string":"Technical Faculty, Bielefeld University, Bielefeld, Germany;","institution_ids":["https://openalex.org/I20121455"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016561889"],"corresponding_institution_ids":["https://openalex.org/I20121455"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":{"value":2195,"currency":"USD","value_usd":2195},"fwci":0.2607,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.61907576,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"37","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9894000291824341,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.987500011920929,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8983820080757141},{"id":"https://openalex.org/keywords/streams","display_name":"STREAMS","score":0.640362024307251},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6296416521072388},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5222542881965637},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4986860752105713},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4668709933757782},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.07143208384513855}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8983820080757141},{"id":"https://openalex.org/C42090638","wikidata":"https://www.wikidata.org/wiki/Q4048907","display_name":"STREAMS","level":2,"score":0.640362024307251},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6296416521072388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5222542881965637},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4986860752105713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4668709933757782},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.07143208384513855}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1080/08839514.2023.2198846","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2023.2198846","pdf_url":"https://www.tandfonline.com/doi/epdf/10.1080/08839514.2023.2198846?needAccess=true&role=button","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:zenodo.org:8288568","is_oa":true,"landing_page_url":"https://zenodo.org/record/8288568","pdf_url":"https://zenodo.org/record/8288568","source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":"APPLIED ARTIFICIAL INTELLIGENCE 37(1)","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:32738ebc4484455c85f7390faaf97305","is_oa":true,"landing_page_url":"https://doaj.org/article/32738ebc4484455c85f7390faaf97305","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":"Applied Artificial Intelligence, Vol 37, Iss 1 (2023)","raw_type":"article"},{"id":"pmh:oai:pub.uni-bielefeld.de:2979026","is_oa":false,"landing_page_url":"https://pub.uni-bielefeld.de/record/2979026","pdf_url":null,"source":{"id":"https://openalex.org/S4306401671","display_name":"PUB \u2013 Publications at Bielefeld University (Bielefeld University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20121455","host_organization_name":"Bielefeld University","host_organization_lineage":["https://openalex.org/I20121455"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"http://purl.org/coar/resource_type/c_6501"}],"best_oa_location":{"id":"doi:10.1080/08839514.2023.2198846","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2023.2198846","pdf_url":"https://www.tandfonline.com/doi/epdf/10.1080/08839514.2023.2198846?needAccess=true&role=button","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.8100000023841858}],"awards":[{"id":"https://openalex.org/G8039044287","display_name":null,"funder_award_id":"Grant agreement No. 951424","funder_id":"https://openalex.org/F4320334678","funder_display_name":"European Research Council"}],"funders":[{"id":"https://openalex.org/F4320334678","display_name":"European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4362723906.pdf","grobid_xml":"https://content.openalex.org/works/W4362723906.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1495858509","https://openalex.org/W1556576706","https://openalex.org/W1656892363","https://openalex.org/W1898424075","https://openalex.org/W1976606095","https://openalex.org/W1980622304","https://openalex.org/W2039434802","https://openalex.org/W2040870580","https://openalex.org/W2068714596","https://openalex.org/W2094150678","https://openalex.org/W2106053110","https://openalex.org/W2120570715","https://openalex.org/W2164141810","https://openalex.org/W2318574661","https://openalex.org/W2329082997","https://openalex.org/W2467405173","https://openalex.org/W2492494189","https://openalex.org/W2585508806","https://openalex.org/W2586931340","https://openalex.org/W2758219826","https://openalex.org/W2771828150","https://openalex.org/W2892623328","https://openalex.org/W2913214128","https://openalex.org/W2951718629","https://openalex.org/W2980139291","https://openalex.org/W2987491803","https://openalex.org/W3084105304","https://openalex.org/W3086133400","https://openalex.org/W3089569838","https://openalex.org/W3095690117","https://openalex.org/W3159243351","https://openalex.org/W3203003849","https://openalex.org/W3207068812","https://openalex.org/W4206580985","https://openalex.org/W4206959827","https://openalex.org/W4210880854","https://openalex.org/W4214601645","https://openalex.org/W4294791681","https://openalex.org/W4294967866","https://openalex.org/W4372292032","https://openalex.org/W6629908152"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4293083682","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"In":[0,121,193],"many":[1],"real-world":[2,203],"scenarios,":[3],"data":[4,22,58,144,162],"are":[5,15],"provided":[6],"as":[7,29,92],"a":[8,24,45,164],"potentially":[9],"infinite":[10],"stream":[11],"of":[12,35,44,56,72,84,151,205],"samples":[13],"that":[14,132,213],"subject":[16],"to":[17,28,47,63,128,138,169,186,195,201],"changes":[18,52],"in":[19,67,80,136,167,190,216],"the":[20,42,48,57,129,140,149,160,199,202,217,226],"underlying":[21],"distribution,":[23],"phenomenon":[25],"often":[26],"referred":[27],"concept":[30,36],"drift.":[31],"A":[32],"specific":[33],"facet":[34],"drift":[37],"is":[38,116],"feature":[39,46,227],"drift,":[40],"where":[41],"relevance":[43,150],"problem":[49,204],"at":[50],"hand":[51],"over":[53],"time.":[54],"High-dimensionality":[55],"poses":[59],"an":[60,118,126],"additional":[61],"challenge":[62],"learning":[64,106,113,135],"algorithms":[65],"operating":[66],"such":[68,91],"environments.":[69],"Common":[70],"scenarios":[71],"this":[73,122],"nature":[74],"can":[75,221],"for":[76,104,114,175],"example":[77],"be":[78,222],"found":[79],"sensor-based":[81],"maintenance":[82],"operations":[83],"industrial":[85],"machines":[86],"or":[87,95],"inside":[88],"entire":[89],"networks,":[90],"power":[93],"grids":[94],"water":[96,206,218],"distribution":[97,207,219],"systems.":[98],"However,":[99],"since":[100],"most":[101],"existing":[102],"methods":[103],"incremental":[105],"focus":[107],"on":[108,143,157,183],"classification":[109],"tasks,":[110],"efficient":[111],"online":[112],"regression":[115],"still":[117],"underdeveloped":[119],"area.":[120],"work,":[123],"we":[124,197,211],"introduce":[125],"extension":[127],"SAM-kNN":[130],"Regressor":[131],"incorporates":[133],"metric":[134],"order":[137,168],"improve":[139,170],"prediction":[141],"quality":[142],"streams,":[145],"gain":[146],"insights":[147],"into":[148,163],"different":[152],"input":[153,161],"features":[154],"and":[155,173],"based":[156],"that,":[158,196],"transform":[159],"lower":[165],"dimension":[166],"computational":[171],"complexity":[172],"suitability":[174],"high-dimensional":[176],"data.":[177],"We":[178],"evaluate":[179],"our":[180,231],"proposed":[181],"method":[182,200],"artificial":[184],"data,":[185],"demonstrate":[187,212],"its":[188],"applicability":[189],"various":[191],"scenarios.":[192],"addition":[194],"apply":[198],"network":[208,220],"monitoring.":[209],"Specifically,":[210],"sensor":[214],"faults":[215],"detected":[223],"by":[224,230],"monitoring":[225],"relevances":[228],"computed":[229],"algorithm.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2025-10-10T00:00:00"}