{"id":"https://openalex.org/W7138348026","doi":"https://doi.org/10.1609/aaai.v40i19.38643","title":"SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection","display_name":"SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138348026","doi":"https://doi.org/10.1609/aaai.v40i19.38643"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i19.38643","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i19.38643","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i19.38643","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129692995","display_name":"Yang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yang Xu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129734595","display_name":"Hang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hang Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129751980","display_name":"Yixiao Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yixiao Ma","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129649816","display_name":"Ye Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye Zhu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129720807","display_name":"Kai Ming Ting","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai Ming Ting","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5129692995"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.5623328,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"19","first_page":"16083","last_page":"16090"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9837999939918518,"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":0.9837999939918518,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0024999999441206455,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.002300000051036477,"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/anomaly-detection","display_name":"Anomaly detection","score":0.567799985408783},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.460999995470047},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44369998574256897},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43709999322891235},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.40119999647140503}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.567799985408783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5422999858856201},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44369998574256897},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43709999322891235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43639999628067017},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36809998750686646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3181000053882599},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2863999903202057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2800999879837036},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i19.38643","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i19.38643","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i19.38643","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i19.38643","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.46598076820373535,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,158],"core":[1],"problem":[2],"in":[3,27,68,73,77,134,149,155,164,195],"multi-view":[4,125],"anomaly":[5],"detection":[6,79,189],"is":[7,52],"to":[8,146,167,178],"represent":[9],"local":[10,25,162],"neighborhoods":[11,121,140,148,154,163],"of":[12,24,70,90],"normal":[13],"instances":[14,126],"consistently":[15],"across":[16,37],"all":[17,38],"views.":[18],"Recent":[19],"approaches":[20],"consider":[21],"a":[22,41,86,104],"representation":[23],"neighborhood":[26],"each":[28],"view":[29],"independently,":[30],"and":[31,137,152,191],"then":[32],"capture":[33,58],"the":[34,64,82,119,139],"consistent":[35,59,120,172],"neighbors":[36,60,66],"views":[39,166],"via":[40],"learning":[42,83],"process.":[43],"They":[44],"suffer":[45],"from":[46],"two":[47,115],"key":[48],"issues.":[49],"First,":[50],"there":[51],"no":[53,129],"guarantee":[54],"that":[55,185],"they":[56],"can":[57],"well,":[61],"especially":[62],"when":[63],"same":[65],"are":[67,122],"regions":[69,151],"varied":[71],"densities":[72],"different":[74,165],"views,":[75],"resulting":[76],"inferior":[78],"accuracy.":[80],"Second,":[81],"process":[84],"has":[85,114,187],"high":[87],"computational":[88],"cost":[89],"O(N^2),":[91],"rendering":[92],"them":[93],"inapplicable":[94],"for":[95],"large":[96,147,196],"datasets.":[97],"To":[98],"address":[99],"these":[100],"issues,":[101],"we":[102],"propose":[103],"novel":[105],"method":[106],"termed":[107],"Spherical":[108],"Consistent":[109],"Neighborhoods":[110],"Ensemble":[111],"(SCoNE).":[112],"It":[113],"unique":[116],"features:":[117],"(a)":[118],"represented":[123,169],"with":[124],"directly,":[127],"requiring":[128],"intermediate":[130],"representations":[131],"as":[132,171],"used":[133],"existing":[135,199],"approaches;":[136],"(b)":[138],"have":[141],"data-dependent":[142,159],"properties,":[143],"which":[144],"lead":[145],"sparse":[150],"small":[153],"dense":[156],"regions.":[157],"properties":[160],"enable":[161],"be":[168],"well":[170],"neighborhoods,":[173],"without":[174],"learning.":[175],"This":[176],"leads":[177],"O(N)":[179],"time":[180],"complexity.":[181],"Empirical":[182],"evaluations":[183],"show":[184],"SCoNE":[186],"superior":[188],"accuracy":[190],"runs":[192],"orders-of-magnitude":[193],"faster":[194],"datasets":[197],"than":[198],"approaches.":[200]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}