{"id":"https://openalex.org/W2734442025","doi":"https://doi.org/10.1109/ijcnn.2017.7966358","title":"A streaming ensemble classifier with multi-class imbalance learning for activity recognition","display_name":"A streaming ensemble classifier with multi-class imbalance learning for activity recognition","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2734442025","doi":"https://doi.org/10.1109/ijcnn.2017.7966358","mag":"2734442025"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5012307714","display_name":"Ahmad Shahi","orcid":null},"institutions":[{"id":"https://openalex.org/I80281795","display_name":"University of Otago","ror":"https://ror.org/01jmxt844","country_code":"NZ","type":"education","lineage":["https://openalex.org/I80281795"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Ahmad Shahi","raw_affiliation_strings":["Department of Information Science, University of Otago, Dunedin, New Zealand","Department of Information Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Science, University of Otago, Dunedin, New Zealand","institution_ids":["https://openalex.org/I80281795"]},{"raw_affiliation_string":"Department of Information Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand#TAB#","institution_ids":["https://openalex.org/I80281795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030040261","display_name":"Jeremiah D. Deng","orcid":"https://orcid.org/0000-0003-3727-4403"},"institutions":[{"id":"https://openalex.org/I80281795","display_name":"University of Otago","ror":"https://ror.org/01jmxt844","country_code":"NZ","type":"education","lineage":["https://openalex.org/I80281795"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Jeremiah D. Deng","raw_affiliation_strings":["Department of Information Science, University of Otago, Dunedin, New Zealand","Department of Information Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Science, University of Otago, Dunedin, New Zealand","institution_ids":["https://openalex.org/I80281795"]},{"raw_affiliation_string":"Department of Information Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand#TAB#","institution_ids":["https://openalex.org/I80281795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061055877","display_name":"Brenden J. Woodford","orcid":null},"institutions":[{"id":"https://openalex.org/I80281795","display_name":"University of Otago","ror":"https://ror.org/01jmxt844","country_code":"NZ","type":"education","lineage":["https://openalex.org/I80281795"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Brenden J. Woodford","raw_affiliation_strings":["Department of Information Science, University of Otago, Dunedin, New Zealand","Department of Information Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Science, University of Otago, Dunedin, New Zealand","institution_ids":["https://openalex.org/I80281795"]},{"raw_affiliation_string":"Department of Information Science, University of Otago, PO Box 56, Dunedin 9054, New Zealand#TAB#","institution_ids":["https://openalex.org/I80281795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I80281795"],"apc_list":null,"apc_paid":null,"fwci":0.4153,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.62389117,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3983","last_page":"3990"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9972000122070312,"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":0.996399998664856,"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":0.7786065340042114},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.6770449876785278},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6662291884422302},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6597372889518738},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5170886516571045},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5109885931015015},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.5003166198730469},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.47497957944869995},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.46344971656799316},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.45594322681427},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.4486272633075714},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.44263792037963867},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40466803312301636}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7786065340042114},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.6770449876785278},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6662291884422302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6597372889518738},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5170886516571045},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5109885931015015},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.5003166198730469},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.47497957944869995},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.46344971656799316},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.45594322681427},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.4486272633075714},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.44263792037963867},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40466803312301636},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W22003769","https://openalex.org/W89348055","https://openalex.org/W1577965913","https://openalex.org/W1969307352","https://openalex.org/W1995710963","https://openalex.org/W2012663856","https://openalex.org/W2052300809","https://openalex.org/W2062021873","https://openalex.org/W2099454382","https://openalex.org/W2104167780","https://openalex.org/W2108892946","https://openalex.org/W2111758928","https://openalex.org/W2118978333","https://openalex.org/W2133864802","https://openalex.org/W2135090840","https://openalex.org/W2148143831","https://openalex.org/W2488983277","https://openalex.org/W2616891418","https://openalex.org/W3014813097","https://openalex.org/W4225941512","https://openalex.org/W4232478844","https://openalex.org/W6600896760","https://openalex.org/W6680007464"],"related_works":["https://openalex.org/W4307392573","https://openalex.org/W2802243998","https://openalex.org/W2736127210","https://openalex.org/W2329342202","https://openalex.org/W2574092225","https://openalex.org/W4200217704","https://openalex.org/W2161835057","https://openalex.org/W1521014365","https://openalex.org/W3208495060","https://openalex.org/W2740428142"],"abstract_inverted_index":{"Stream":[0],"multi-class":[1,19,33,95],"imbalance":[2,20,97],"learning":[3,11,29,69,122],"in":[4,27,44,84,121,133],"smart":[5,183],"home":[6,184],"applications":[7],"is":[8,40,50,104,156,169],"an":[9,58],"evolving":[10],"area":[12],"that":[13,35],"incorporates":[14],"the":[15,28,31,41,55,101,118,127,135,175,178],"challenges":[16],"of":[17,57,61,117,177],"both":[18],"and":[21,65,155,168],"stream":[22,48,64,86,96],"learning.":[23],"Moreover,":[24],"another":[25],"argument":[26],"from":[30,70,115,141],"imbalanced":[32,42],"distributions":[34],"cause":[36],"misleading":[37],"classification":[38],"outcomes,":[39],"ratio":[43],"a":[45,74,82,85,93,105,164],"sensor":[46,62],"data":[47,63,72,80],"which":[49],"vigorously":[51],"changing.":[52],"Due":[53],"to":[54,77,131,146,149,159,171],"presence":[56],"inadequate":[59],"representation":[60],"class":[66],"distribution":[67],"skews,":[68],"such":[71],"entails":[73],"new":[75,94],"algorithm":[76],"transform":[78],"balanced":[79],"into":[81],"model":[83],"fashion.":[87],"In":[88,109],"this":[89,110],"paper,":[90],"we":[91],"propose":[92],"ensemble":[98],"method":[99,139,180,189],"where":[100],"base":[102],"learner":[103],"Na\u00efve":[106],"Bayesian":[107],"classifier.":[108],"approach,":[111],"each":[112],"training":[113],"instance":[114],"any":[116],"classes":[119],"involved":[120],"based":[123],"on":[124,126,181],"thresholding":[125],"median":[128],"prior":[129],"probability":[130],"aid":[132],"balancing":[134],"classes.":[136],"Our":[137,187],"proposed":[138,179,188],"diverges":[140],"state-of-the-art":[142,193],"approaches":[143],"with":[144,192],"regard":[145],"being":[147],"robust":[148],"outliers,":[150],"retains":[151],"more":[152],"useful":[153],"information,":[154],"less":[157],"sensitive":[158],"over-fitting.":[160],"Also,":[161],"it":[162],"has":[163],"simple":[165],"conceptual":[166],"justification":[167],"easy":[170],"implement.":[172],"We":[173],"illustrate":[174],"effectiveness":[176],"two":[182],"testbed":[185],"datasets.":[186],"compares":[190],"favourably":[191],"approaches.":[194]},"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":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}