{"id":"https://openalex.org/W7131423960","doi":"https://doi.org/10.1109/icdm65498.2025.00163","title":"WWAggr: A Window Wasserstein-Based Aggregation for Ensemble Change Point Detection","display_name":"WWAggr: A Window Wasserstein-Based Aggregation for Ensemble Change Point Detection","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7131423960","doi":"https://doi.org/10.1109/icdm65498.2025.00163"},"language":null,"primary_location":{"id":"doi:10.1109/icdm65498.2025.00163","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00163","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 (ICDM)","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/A5035507975","display_name":"Alexander Stepikin","orcid":null},"institutions":[{"id":"https://openalex.org/I9115533","display_name":"Moscow Polytechnic University","ror":"https://ror.org/03paz2a60","country_code":"RU","type":"education","lineage":["https://openalex.org/I9115533"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Alexander Stepikin","raw_affiliation_strings":["Applied AI Center, Skoltech,Moscow,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied AI Center, Skoltech,Moscow,Russia","institution_ids":["https://openalex.org/I9115533"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Evgenia Romanenkova","orcid":null},"institutions":[{"id":"https://openalex.org/I9115533","display_name":"Moscow Polytechnic University","ror":"https://ror.org/03paz2a60","country_code":"RU","type":"education","lineage":["https://openalex.org/I9115533"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Evgenia Romanenkova","raw_affiliation_strings":["Applied AI Center, Skoltech,Moscow,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied AI Center, Skoltech,Moscow,Russia","institution_ids":["https://openalex.org/I9115533"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011905327","display_name":"Alexey Zaytsev","orcid":"https://orcid.org/0000-0002-1653-0204"},"institutions":[{"id":"https://openalex.org/I9115533","display_name":"Moscow Polytechnic University","ror":"https://ror.org/03paz2a60","country_code":"RU","type":"education","lineage":["https://openalex.org/I9115533"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Alexey Zaytsev","raw_affiliation_strings":["Applied AI Center, Skoltech,Moscow,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied AI Center, Skoltech,Moscow,Russia","institution_ids":["https://openalex.org/I9115533"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":0.82114991,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1535","last_page":"1544"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.31869998574256897,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.31869998574256897,"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":0.11760000139474869,"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"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.03869999945163727,"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/boosting","display_name":"Boosting (machine learning)","score":0.7307999730110168},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.555899977684021},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.5321000218391418},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4510999917984009},{"id":"https://openalex.org/keywords/lift","display_name":"Lift (data mining)","score":0.4357999861240387},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.43149998784065247},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36480000615119934},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3237999975681305}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7307999730110168},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6220999956130981},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.555899977684021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5408999919891357},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.5321000218391418},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45080000162124634},{"id":"https://openalex.org/C139002025","wikidata":"https://www.wikidata.org/wiki/Q3001212","display_name":"Lift (data mining)","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.43149998784065247},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33889999985694885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3061999976634979},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.26249998807907104}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm65498.2025.00163","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00163","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 (ICDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.4699508845806122,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G1550044415","display_name":null,"funder_award_id":"000000C313925P4F0002","funder_id":"https://openalex.org/F4320324632","funder_display_name":"Ministero dello Sviluppo Economico"},{"id":"https://openalex.org/G5309637246","display_name":null,"funder_award_id":"139-10-2025-033","funder_id":"https://openalex.org/F4320324442","funder_display_name":"Skolkovo Institute of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320324442","display_name":"Skolkovo Institute of Science and Technology","ror":"https://ror.org/03f9nc143"},{"id":"https://openalex.org/F4320324632","display_name":"Ministero dello Sviluppo Economico","ror":"https://ror.org/011z3ff80"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2203250586","https://openalex.org/W2773235741","https://openalex.org/W2909693411","https://openalex.org/W2962688977","https://openalex.org/W2962736036","https://openalex.org/W2962951775","https://openalex.org/W2963416868","https://openalex.org/W2963795951","https://openalex.org/W2973214398","https://openalex.org/W2990503944","https://openalex.org/W3043426275","https://openalex.org/W3109518304","https://openalex.org/W3149839747","https://openalex.org/W3152994159","https://openalex.org/W3159888063","https://openalex.org/W3161665949","https://openalex.org/W3198435832","https://openalex.org/W3199148273","https://openalex.org/W4206356216","https://openalex.org/W4206992850","https://openalex.org/W4226377949","https://openalex.org/W4247726808","https://openalex.org/W4249116379","https://openalex.org/W4280494023","https://openalex.org/W4304098235","https://openalex.org/W4386952058","https://openalex.org/W4392640438","https://openalex.org/W4406432825","https://openalex.org/W4407832074"],"related_works":[],"abstract_inverted_index":{"Change":[0],"Point":[1],"Detection":[2],"(CPD)":[3],"aims":[4],"to":[5,21,31,42,79],"identify":[6],"moments":[7],"of":[8,27,61,93,110,124],"abrupt":[9],"distribution":[10],"shifts":[11],"in":[12],"data":[13,22],"streams.":[14],"Real-world":[15],"high-dimensional":[16],"CPD":[17,112],"remains":[18],"challenging":[19],"due":[20],"pattern":[23],"complexity":[24],"and":[25,66,77],"violation":[26],"common":[28],"assumptions.":[29],"Resorting":[30],"standalone":[32],"deep":[33,62,111],"neural":[34],"networks,":[35],"the":[36,53,98,125],"current":[37],"state-of-theart":[38],"detectors":[39,65],"have":[40],"yet":[41],"achieve":[43],"perfect":[44],"quality.":[45],"Concurrently,":[46],"ensembling":[47],"provides":[48],"more":[49],"robust":[50],"solutions,":[51,117],"boosting":[52],"performance.":[54],"In":[55],"this":[56],"paper,":[57],"we":[58,85,118],"investigate":[59],"ensembles":[60,109],"change":[63],"point":[64],"realize":[67],"that":[68],"standard":[69],"prediction":[70],"aggregation":[71,95],"techniques,":[72],"e.g.,":[73],"averaging,":[74],"are":[75],"suboptimal":[76],"fail":[78],"account":[80],"for":[81,129],"problem":[82,123],"peculiarities.":[83],"Alternatively,":[84],"introduce":[86],"WWAggr":[87],"-":[88],"a":[89,121],"novel":[90],"task-specific":[91],"method":[92],"ensemble":[94],"based":[96],"on":[97],"Wasserstein":[99],"distance.":[100],"Our":[101],"procedure":[102],"is":[103],"versatile,":[104],"working":[105],"effectively":[106],"with":[107],"various":[108],"models.":[113],"Moreover,":[114],"unlike":[115],"existing":[116],"practically":[119],"lift":[120],"long-standing":[122],"decision":[126],"threshold":[127],"selection":[128],"CPD.":[130]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-26T00:00:00"}
