{"id":"https://openalex.org/W2735334134","doi":"https://doi.org/10.1109/ijcnn.2017.7965964","title":"A partial labeling framework for multi-class imbalanced streaming data","display_name":"A partial labeling framework for multi-class imbalanced streaming data","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735334134","doi":"https://doi.org/10.1109/ijcnn.2017.7965964","mag":"2735334134"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7965964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965964","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":"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/A5068281225","display_name":"Elaheh Arabmakki","orcid":null},"institutions":[{"id":"https://openalex.org/I142740786","display_name":"University of Louisville","ror":"https://ror.org/01ckdn478","country_code":"US","type":"education","lineage":["https://openalex.org/I142740786"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Elaheh Arabmakki","raw_affiliation_strings":["Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA","institution_ids":["https://openalex.org/I142740786"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065596336","display_name":"Mehmed Kantardzic","orcid":"https://orcid.org/0000-0002-6861-4434"},"institutions":[{"id":"https://openalex.org/I142740786","display_name":"University of Louisville","ror":"https://ror.org/01ckdn478","country_code":"US","type":"education","lineage":["https://openalex.org/I142740786"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mehmed Kantardzic","raw_affiliation_strings":["Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA","institution_ids":["https://openalex.org/I142740786"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048979573","display_name":"Tegjyot Singh Sethi","orcid":"https://orcid.org/0000-0001-5757-7917"},"institutions":[{"id":"https://openalex.org/I142740786","display_name":"University of Louisville","ror":"https://ror.org/01ckdn478","country_code":"US","type":"education","lineage":["https://openalex.org/I142740786"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tegjyot Singh Sethi","raw_affiliation_strings":["Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA","institution_ids":["https://openalex.org/I142740786"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068281225"],"corresponding_institution_ids":["https://openalex.org/I142740786"],"apc_list":null,"apc_paid":null,"fwci":0.6192,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.75570302,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"2","issue":null,"first_page":"1018","last_page":"1025"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9896000027656555,"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":0.9855999946594238,"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":0.7297600507736206},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5976026058197021},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.5887228846549988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40684980154037476},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2624337077140808}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7297600507736206},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5976026058197021},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.5887228846549988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40684980154037476},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2624337077140808}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7965964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965964","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W64908097","https://openalex.org/W1486002488","https://openalex.org/W1518640223","https://openalex.org/W1565746575","https://openalex.org/W1605695115","https://openalex.org/W2042593695","https://openalex.org/W2060206013","https://openalex.org/W2097987924","https://openalex.org/W2099419573","https://openalex.org/W2104933073","https://openalex.org/W2106136950","https://openalex.org/W2119191234","https://openalex.org/W2132870739","https://openalex.org/W2153635508","https://openalex.org/W2154134600","https://openalex.org/W2158081225","https://openalex.org/W2171809276","https://openalex.org/W2172000360","https://openalex.org/W2185967890","https://openalex.org/W2249110300","https://openalex.org/W2526823171","https://openalex.org/W2562656267","https://openalex.org/W2591110321","https://openalex.org/W3120740533","https://openalex.org/W4254721730","https://openalex.org/W6675634716","https://openalex.org/W6685747873","https://openalex.org/W6691714544"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2384129116","https://openalex.org/W2381462238"],"abstract_inverted_index":{"Imbalanced":[0],"data":[1,23,27,107,112,124,149,184],"streams":[2],"are":[3,137],"found":[4],"in":[5],"many":[6],"real":[7],"world":[8],"applications":[9],"such":[10,22],"as":[11],"spam":[12],"email":[13],"detection,":[14],"and":[15,31,61,69,104,116,134,147],"internet":[16],"traffic":[17],"data.":[18],"The":[19,151],"classification":[20,99,143],"of":[21,46,66,122,144,158,175,182],"is":[24,54,59,97,130,161],"challenging,":[25],"since":[26,57],"stream":[28],"usually":[29],"changes,":[30],"the":[32,39,43,47,67,102,111,123,127,145,156,159,166,176,183],"model":[33,53],"should":[34],"be":[35],"updated":[36],"to":[37,49,74,82,125,173,178],"maintain":[38],"performance.":[40],"However,":[41],"obtaining":[42],"true":[44],"labels":[45],"samples":[48,177],"build":[50],"a":[51,98,119],"new":[52],"not":[55,162],"easy,":[56],"labeling":[58,140],"expensive":[60],"time":[62],"consuming.":[63],"Additionally,":[64],"existence":[65],"multiple":[68,114],"imbalanced":[70,106,146],"classes":[71],"may":[72],"cause":[73],"lose":[75],"performance":[76,157],"over":[77],"one":[78],"class":[79],"while":[80],"trying":[81],"gain":[83],"on":[84,186],"another.":[85],"In":[86],"this":[87],"paper,":[88],"we":[89],"propose":[90],"RLS-Multi":[91,109,129,160],"(Reduced":[92],"Labeled":[93],"Samples-Multiple":[94],"class)":[95],"which":[96,136],"framework":[100],"for":[101,142,180],"multi-class":[103,148],"evolving":[105],"stream.":[108,150],"handles":[110],"with":[113,132],"classes,":[115],"it":[117],"uses":[118],"small":[120],"fraction":[121],"update":[126],"model.":[128],"compared":[131],"McELM,":[133],"VWOS-ELM":[135],"two":[138,167],"fully":[139],"approaches":[141],"experimental":[152],"results":[153],"show":[154],"that":[155],"significantly":[163],"different":[164],"from":[165],"other":[168],"techniques,":[169],"requiring":[170],"only":[171],"up":[172],"25%":[174],"label":[179],"majority":[181],"sets,":[185],"average.":[187]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
