{"id":"https://openalex.org/W4408566096","doi":"https://doi.org/10.1109/icdmw65004.2024.00091","title":"Anomaly Detection and Interpretation from Tabular Data Using Transformer Architecture","display_name":"Anomaly Detection and Interpretation from Tabular Data Using Transformer Architecture","publication_year":2024,"publication_date":"2024-12-09","ids":{"openalex":"https://openalex.org/W4408566096","doi":"https://doi.org/10.1109/icdmw65004.2024.00091"},"language":"en","primary_location":{"id":"doi:10.1109/icdmw65004.2024.00091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw65004.2024.00091","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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/A5001028658","display_name":"Hajar Homayouni","orcid":"https://orcid.org/0000-0001-8898-9498"},"institutions":[{"id":"https://openalex.org/I26538001","display_name":"San Diego State University","ror":"https://ror.org/0264fdx42","country_code":"US","type":"education","lineage":["https://openalex.org/I26538001"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hajar Homayouni","raw_affiliation_strings":["San Diego State University"],"affiliations":[{"raw_affiliation_string":"San Diego State University","institution_ids":["https://openalex.org/I26538001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116678368","display_name":"Hamed Aghayarzadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hamed Aghayarzadeh","raw_affiliation_strings":["Colorado State University"],"affiliations":[{"raw_affiliation_string":"Colorado State University","institution_ids":["https://openalex.org/I92446798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008904412","display_name":"Indrakshi Ray","orcid":"https://orcid.org/0000-0002-0714-7676"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Indrakshi Ray","raw_affiliation_strings":["Colorado State University"],"affiliations":[{"raw_affiliation_string":"Colorado State University","institution_ids":["https://openalex.org/I92446798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047638523","display_name":"Hossein Shirazi","orcid":"https://orcid.org/0000-0002-2721-0628"},"institutions":[{"id":"https://openalex.org/I26538001","display_name":"San Diego State University","ror":"https://ror.org/0264fdx42","country_code":"US","type":"education","lineage":["https://openalex.org/I26538001"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hossein Shirazi","raw_affiliation_strings":["San Diego State University"],"affiliations":[{"raw_affiliation_string":"San Diego State University","institution_ids":["https://openalex.org/I26538001"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5001028658"],"corresponding_institution_ids":["https://openalex.org/I26538001"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71513088,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"657","last_page":"664"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.982200026512146,"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":0.982200026512146,"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/T13650","display_name":"Computational Physics and Python Applications","score":0.9225999712944031,"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":0.6760702133178711},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6287144422531128},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5367210507392883},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5237234234809875},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.4566073417663574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33926647901535034},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.1548263430595398},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13890033960342407},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.10730615258216858},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.10155034065246582},{"id":"https://openalex.org/keywords/archaeology","display_name":"Archaeology","score":0.07969295978546143},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.06994059681892395}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6760702133178711},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6287144422531128},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5367210507392883},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5237234234809875},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.4566073417663574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33926647901535034},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1548263430595398},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13890033960342407},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.10730615258216858},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.10155034065246582},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.07969295978546143},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.06994059681892395}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw65004.2024.00091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw65004.2024.00091","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Data Mining Workshops (ICDMW)","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":24,"referenced_works":["https://openalex.org/W1970088130","https://openalex.org/W2064675550","https://openalex.org/W2122646361","https://openalex.org/W2725449579","https://openalex.org/W2743138268","https://openalex.org/W2970726176","https://openalex.org/W2985323229","https://openalex.org/W3040266635","https://openalex.org/W3109037541","https://openalex.org/W3118485687","https://openalex.org/W3159922383","https://openalex.org/W3164952570","https://openalex.org/W3173787059","https://openalex.org/W3202257884","https://openalex.org/W4205388843","https://openalex.org/W4367016885","https://openalex.org/W4387847105","https://openalex.org/W4387848578","https://openalex.org/W4387848702","https://openalex.org/W6797867632","https://openalex.org/W6809446827","https://openalex.org/W6838847448","https://openalex.org/W6845816046","https://openalex.org/W6859685042"],"related_works":["https://openalex.org/W4313320911","https://openalex.org/W4327743144","https://openalex.org/W4245077728","https://openalex.org/W2607424049","https://openalex.org/W4390922876","https://openalex.org/W3183204001","https://openalex.org/W4206302830","https://openalex.org/W2185941092","https://openalex.org/W4386782890","https://openalex.org/W3210948575"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,64,177],"in":[2,8,119,124],"tabular":[3,37,66,85],"data":[4,38,51,67,121],"are":[5,159],"often":[6,24],"needed":[7],"most":[9],"domains,":[10],"including":[11,179],"finance,":[12],"medicine,":[13],"and":[14,23,34,68,140,193,211],"engineering.":[15],"However,":[16],"traditional":[17],"methods":[18,237],"typically":[19],"require":[20],"intensive":[21],"processing":[22],"lack":[25],"interpretability,":[26],"which":[27],"limits":[28],"their":[29,142],"practicality.":[30],"As":[31],"the":[32,88,128,135,151,206],"complexity":[33],"volume":[35],"of":[36,65],"increase,":[39],"there":[40],"is":[41],"a":[42,59,75,79,131,145,220],"need":[43],"for":[44,62,84,243],"advanced":[45],"techniques":[46],"that":[47,227],"can":[48],"handle":[49],"diverse":[50],"types":[52],"while":[53,238],"offering":[54,240],"interpretable":[55,241],"results.":[56],"We":[57,170],"introduce":[58],"novel":[60,221],"approach":[61,173,229],"anomaly":[63,72,107,176],"its":[69,96,138],"interpretation.":[70],"The":[71],"detector":[73],"employs":[74],"transformer":[76],"model":[77,129],"with":[78],"custom":[80],"embedding":[81],"layer":[82],"tailored":[83],"data.":[86,126],"While":[87],"model\u2019s":[89],"attention":[90,111],"weights":[91,112],"do":[92],"not":[93],"directly":[94],"explain":[95],"decision-making":[97],"process,":[98],"they":[99],"offer":[100],"useful":[101],"insights":[102],"when":[103],"interpreted":[104],"thoughtfully.":[105],"Our":[106,200],"interpreter":[108,136],"uses":[109,202],"these":[110],"to":[113,233],"identify":[114],"irregularities":[115],"by":[116],"comparing":[117],"patterns":[118],"anomalous":[120],"against":[122,174],"those":[123],"normal":[125,152],"When":[127],"flags":[130],"row":[132],"as":[133,161],"anomalous,":[134],"analyzes":[137],"columns":[139],"assesses":[141],"relationships":[143,167],"using":[144,216],"reference":[146],"association":[147],"matrix":[148],"created":[149],"from":[150,156,205],"dataset.":[153],"Any":[154],"deviations":[155],"expected":[157],"associations":[158],"flagged":[160],"potential":[162],"rule":[163],"violations,":[164],"highlighting":[165],"unusual":[166],"between":[168],"columns.":[169],"evaluate":[171],"our":[172,228],"baseline":[175],"techniques,":[178],"Multi-Layer":[180],"Perceptrons":[181],"(MLPs),":[182],"Long":[183],"Short-Term":[184],"Memory":[185],"(LSTM)":[186],"networks,":[187],"One-Class":[188],"Support":[189,195],"Vector":[190,196],"Machines":[191],"(OC-SVM),":[192],"Deep":[194],"Data":[197],"Description":[198],"(Deep-SVDD).":[199],"experiment":[201],"labeled":[203],"datasets":[204],"Outlier":[207],"Detection":[208],"DataSets":[209],"(ODDS),":[210],"KDD":[212],"datasets,":[213],"assessing":[214],"performance":[215],"standard":[217],"metrics":[218],"alongside":[219],"mutation":[222],"analysis":[223],"technique.":[224],"Results":[225],"indicate":[226],"achieves":[230],"accuracy":[231],"comparable":[232],"or":[234],"surpassing":[235],"existing":[236],"also":[239],"explanations":[242],"detected":[244],"anomalies.":[245]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
