{"id":"https://openalex.org/W7090220549","doi":"https://doi.org/10.1109/fuzz62266.2025.11197685","title":"Interpreting and Unifying Anomaly Scores with Antagonistic Fuzzy Sets","display_name":"Interpreting and Unifying Anomaly Scores with Antagonistic Fuzzy Sets","publication_year":2025,"publication_date":"2025-07-06","ids":{"openalex":"https://openalex.org/W7090220549","doi":"https://doi.org/10.1109/fuzz62266.2025.11197685"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz62266.2025.11197685","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11197685","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 Fuzzy Systems (FUZZ)","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":null,"display_name":"F\u00e9lix Iglesias V\u00e1zquez","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122474","display_name":"Forschungszentrum Telekommunikation Wien","ror":"https://ror.org/026nftk33","country_code":"AT","type":"facility","lineage":["https://openalex.org/I4210122474"]},{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"F\u00e9lix Iglesias V\u00e1zquez","raw_affiliation_strings":["Inst. of Telecommun, TU Wien,Vienna,Austria"],"affiliations":[{"raw_affiliation_string":"Inst. of Telecommun, TU Wien,Vienna,Austria","institution_ids":["https://openalex.org/I4210122474","https://openalex.org/I145847075"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tanja Zseby","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122474","display_name":"Forschungszentrum Telekommunikation Wien","ror":"https://ror.org/026nftk33","country_code":"AT","type":"facility","lineage":["https://openalex.org/I4210122474"]},{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Tanja Zseby","raw_affiliation_strings":["Inst. of Telecommun, TU Wien,Vienna,Austria"],"affiliations":[{"raw_affiliation_string":"Inst. of Telecommun, TU Wien,Vienna,Austria","institution_ids":["https://openalex.org/I4210122474","https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":null,"display_name":"Arthur Zimek","orcid":null},"institutions":[{"id":"https://openalex.org/I177969490","display_name":"University of Southern Denmark","ror":"https://ror.org/03yrrjy16","country_code":"DK","type":"education","lineage":["https://openalex.org/I177969490"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Arthur Zimek","raw_affiliation_strings":["University of Southern Denmark (SDU),Dep. of Math. and Comp. Sci. (IMADA),Odense,Denmark"],"affiliations":[{"raw_affiliation_string":"University of Southern Denmark (SDU),Dep. of Math. and Comp. Sci. (IMADA),Odense,Denmark","institution_ids":["https://openalex.org/I177969490"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145847075","https://openalex.org/I4210122474"],"apc_list":null,"apc_paid":null,"fwci":2.6995,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.94032529,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.8323000073432922,"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":0.8323000073432922,"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":0.0215000007301569,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.013700000010430813,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/vagueness","display_name":"Vagueness","score":0.7516000270843506},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.59579998254776},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.578499972820282},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5773000121116638},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.48910000920295715},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4431000053882599},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.41760000586509705}],"concepts":[{"id":"https://openalex.org/C2776825360","wikidata":"https://www.wikidata.org/wiki/Q1411921","display_name":"Vagueness","level":3,"score":0.7516000270843506},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.59579998254776},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.578499972820282},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5773000121116638},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5138999819755554},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5098000168800354},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.48910000920295715},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4431000053882599},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4242999851703644},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39640000462532043},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C45983554","wikidata":"https://www.wikidata.org/wiki/Q3412851","display_name":"Information quality","level":3,"score":0.2775000035762787},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2689000070095062}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz62266.2025.11197685","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11197685","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 Fuzzy Systems (FUZZ)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W21026159","https://openalex.org/W96556717","https://openalex.org/W164607750","https://openalex.org/W2041184937","https://openalex.org/W2045765911","https://openalex.org/W2129281431","https://openalex.org/W2144182447","https://openalex.org/W2160868604","https://openalex.org/W2282861635","https://openalex.org/W2296719434","https://openalex.org/W2769911895","https://openalex.org/W2888297916","https://openalex.org/W3130041243","https://openalex.org/W4295768095","https://openalex.org/W4312433903","https://openalex.org/W4384784915","https://openalex.org/W4394761939","https://openalex.org/W4400824028","https://openalex.org/W4403716417","https://openalex.org/W4404671215"],"related_works":[],"abstract_inverted_index":{"Normalizing":[0],"anomaly":[1,59,115],"scores":[2,40,103,175],"as":[3,65,79,137],"probability":[4],"estimates":[5],"has":[6,169],"become":[7,17],"a":[8,29,45,54,63,66,97,164],"standard":[9],"practice.":[10],"Scores":[11],"turn":[12],"into":[13],"interpretable":[14],"values":[15],"and":[16,32,49,105,116,183,201,218],"suitable":[18],"to":[19,71,99,148,203],"build":[20],"ensembles.":[21],"While":[22],"this":[23,89],"is":[24],"undoubtedly":[25],"useful,":[26],"it":[27,68],"introduces":[28],"conceptual":[30,165],"imprecision":[31],"imposes":[33],"unnecessary":[34,92],"constraints.":[35],"Algorithms":[36],"usually":[37],"output":[38],"raw":[39],"that":[41,102,154,195],"are":[42,118,133,155],"measures":[43,138],"of":[44,62,139,152,173],"data":[46],"space":[47],"property,":[48],"do":[50],"not":[51],"necessarily":[52],"reflect":[53],"probability.":[55],"By":[56],"modeling":[57],"the":[58,100,150,170],"(a)":[60],"quality":[61,78],"point":[64],"probability,":[67],"naturally":[69],"follows":[70],"portray":[72],"its":[73,185],"normal":[74,117,157],"(<tex":[75,81],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[76,82,126],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$n$</tex>)":[77],"complementary":[80],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\forall":[83],"a,":[84],"n":[85,128],"\\quad":[86],"a+n=1$</tex>).":[87],"However,":[88],"causes":[90],"an":[91,111],"tension":[93],"between":[94],"both":[95],"variables,":[96],"detriment":[98],"information":[101,178,200],"store":[104],"even":[106],"potential":[107],"misinterpretations.":[108],"We":[109,180],"propose":[110],"alternative":[112],"normalization":[113],"where":[114],"antagonistic":[119],"fuzzy":[120],"sets":[121],"independently":[122],"modeled,":[123],"i.e.,":[124],"<tex":[125],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$a,":[127],"\\in[0,":[129],"1]$</tex>.":[130],"Thus,":[131],"they":[132],"more":[134,199],"consistently":[135],"interpreted":[136],"vagueness":[140],"rather":[141],"than":[142],"uncertainty.":[143],"This":[144],"allows,":[145],"for":[146],"example,":[147],"acknowledge":[149],"existence":[151],"samples":[153],"neither":[156],"nor":[158],"anomalous":[159],"(e.g.,":[160],"noise).":[161],"Beyond":[162],"overcoming":[163],"problem,":[166],"our":[167,196],"proposal":[168],"practical":[171],"benefit":[172],"unifying":[174],"while":[176],"minimizing":[177],"loss.":[179],"show,":[181],"evaluate":[182],"compare":[184],"effects":[186],"with":[187,211],"47":[188],"popular":[189],"field":[190],"dataset":[191],"benchmarks.":[192],"Results":[193],"confirm":[194],"approach":[197],"retains":[198],"leads":[202],"better":[204,215],"performances.":[205],"Anomaly":[206],"membership":[207],"degrees":[208],"yield":[209],"ensembles":[210],"enhanced":[212],"overall":[213],"ranking,":[214],"precision-recall":[216],"balance,":[217],"higher":[219],"precision":[220],"in":[221],"top":[222],"scores.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-14T00:00:00"}