{"id":"https://openalex.org/W7134927120","doi":"https://doi.org/10.1109/icdmw69685.2025.00037","title":"Flexible and Efficient Drift Detection Without Labels","display_name":"Flexible and Efficient Drift Detection Without Labels","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7134927120","doi":"https://doi.org/10.1109/icdmw69685.2025.00037"},"language":null,"primary_location":{"id":"doi:10.1109/icdmw69685.2025.00037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","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/A5128692813","display_name":"Nelvin Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210149506","display_name":"American Express (United States)","ror":"https://ror.org/05rckx884","country_code":"US","type":"company","lineage":["https://openalex.org/I4210149506"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nelvin Tan","raw_affiliation_strings":["American Express"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American Express","institution_ids":["https://openalex.org/I4210149506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120364286","display_name":"Yu-Ching Shih","orcid":null},"institutions":[{"id":"https://openalex.org/I4210149506","display_name":"American Express (United States)","ror":"https://ror.org/05rckx884","country_code":"US","type":"company","lineage":["https://openalex.org/I4210149506"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu-Ching Shih","raw_affiliation_strings":["American Express"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American Express","institution_ids":["https://openalex.org/I4210149506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128755541","display_name":"Dong Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210149506","display_name":"American Express (United States)","ror":"https://ror.org/05rckx884","country_code":"US","type":"company","lineage":["https://openalex.org/I4210149506"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Yang","raw_affiliation_strings":["American Express"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American Express","institution_ids":["https://openalex.org/I4210149506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5120656459","display_name":"Amol Salunkhe","orcid":null},"institutions":[{"id":"https://openalex.org/I4210149506","display_name":"American Express (United States)","ror":"https://ror.org/05rckx884","country_code":"US","type":"company","lineage":["https://openalex.org/I4210149506"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amol Salunkhe","raw_affiliation_strings":["American Express"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American Express","institution_ids":["https://openalex.org/I4210149506"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82760181,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"265","last_page":"274"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.13600000739097595,"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":0.13600000739097595,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.03139999881386757,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.026399999856948853,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.36169999837875366},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.29580000042915344},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.28760001063346863},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.26089999079704285},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.25380000472068787}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5367000102996826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4578000009059906},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36489999294281006},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.36169999837875366},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29739999771118164},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.24199999868869781}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw69685.2025.00037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.5948063731193542,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1585854823","https://openalex.org/W1642701485","https://openalex.org/W1873218251","https://openalex.org/W2001150023","https://openalex.org/W2004816234","https://openalex.org/W2052283750","https://openalex.org/W2075721911","https://openalex.org/W2099419573","https://openalex.org/W2109964623","https://openalex.org/W2143991132","https://openalex.org/W2157664934","https://openalex.org/W2310485631","https://openalex.org/W2487770199","https://openalex.org/W2605253252","https://openalex.org/W2620064622","https://openalex.org/W2767528836","https://openalex.org/W2891560655","https://openalex.org/W2898017895","https://openalex.org/W2939366709","https://openalex.org/W3044058243","https://openalex.org/W4206471589"],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1,28],"models":[2,20,80],"are":[3,64,94],"being":[4],"increasingly":[5],"used":[6,81],"to":[7,82,121,152],"automate":[8],"decisions":[9],"in":[10,117],"almost":[11],"every":[12],"domain,":[13],"and":[14,105,164],"ensuring":[15,24],"the":[16,40,53,58,76,91,154],"performance":[17,77,182],"of":[18,39,45,61,78,156],"these":[19],"is":[21,34,37],"crucial":[22],"for":[23,71],"high":[25],"quality":[26],"machine":[27],"enabled":[29],"services.":[30],"Ensuring":[31],"concept":[32,48,107,124],"drift":[33,49,108,149,158,169],"detected":[35],"early":[36],"thus":[38],"highest":[41],"importance.":[42],"A":[43],"lot":[44],"research":[46],"on":[47,52],"has":[50,135],"focused":[51],"supervised":[54,62],"case":[55],"that":[56,111,129],"assumes":[57],"true":[59,92],"labels":[60,93],"tasks":[63],"available":[65],"immediately":[66],"after":[67],"making":[68],"predictions.":[69],"Controlling":[70],"false":[72],"positives":[73],"while":[74],"monitoring":[75],"predictive":[79],"make":[83],"inference":[84],"from":[85],"extremely":[86,99],"large":[87],"datasets":[88],"periodically,":[89],"where":[90],"not":[95],"instantly":[96],"available,":[97],"becomes":[98],"challenging.":[100],"We":[101,126,179],"propose":[102],"a":[103,118,146],"flexible":[104],"efficient":[106],"detection":[109,150,170],"algorithm":[110,171],"uses":[112],"classical":[113],"statistical":[114,137],"process":[115],"control":[116],"label-less":[119],"setting":[120],"accurately":[122],"detect":[123],"drifts.":[125],"shown":[127],"empirically":[128],"under":[130],"computational":[131],"constraints,":[132],"our":[133,166,168],"approach":[134],"better":[136],"power":[138],"than":[139],"previous":[140],"known":[141],"methods.":[142],"Furthermore,":[143],"we":[144],"introduce":[145],"new":[147],"semi-supervised":[148],"framework":[151],"model":[153],"scenario":[155],"detecting":[157],"(without":[159],"labels)":[160],"given":[161],"prior":[162],"detections,":[163],"show":[165],"how":[167],"can":[172],"be":[173],"incorporated":[174],"effectively":[175],"into":[176],"this":[177],"framework.":[178],"demonstrate":[180],"promising":[181],"via":[183],"numerical":[184],"simulations.":[185]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-12T00:00:00"}