{"id":"https://openalex.org/W3121941635","doi":"https://doi.org/10.1109/itc44778.2020.9325225","title":"Advanced Outlier Detection Using Unsupervised Learning for Screening Potential Customer Returns","display_name":"Advanced Outlier Detection Using Unsupervised Learning for Screening Potential Customer Returns","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3121941635","doi":"https://doi.org/10.1109/itc44778.2020.9325225","mag":"3121941635"},"language":"en","primary_location":{"id":"doi:10.1109/itc44778.2020.9325225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itc44778.2020.9325225","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Test Conference (ITC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5073026003","display_name":"Hanbin Hu","orcid":"https://orcid.org/0000-0003-4223-5898"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanbin Hu","raw_affiliation_strings":["Department of ECE, University of California, Santa Barbara, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of ECE, University of California, Santa Barbara, CA","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107446421","display_name":"Nguyen Nguyen","orcid":"https://orcid.org/0000-0003-1553-3224"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen Nguyen","raw_affiliation_strings":["NXP Semiconductors, Austin, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NXP Semiconductors, Austin, TX","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105462297","display_name":"Chen He","orcid":"https://orcid.org/0000-0001-8100-0237"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen He","raw_affiliation_strings":["NXP Semiconductors, Austin, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NXP Semiconductors, Austin, TX","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100432727","display_name":"Peng Li","orcid":"https://orcid.org/0000-0002-7181-7304"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Li","raw_affiliation_strings":["Department of ECE, University of California, Santa Barbara, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of ECE, University of California, Santa Barbara, CA","institution_ids":["https://openalex.org/I154570441"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2020","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9902999997138977,"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"}},{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7477145195007324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6690842509269714},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5910528302192688},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5494292974472046},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5355626940727234},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.533820629119873},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.527588427066803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4918398857116699},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4908373951911926},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4284508228302002},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4113563299179077},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4084276854991913}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7477145195007324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6690842509269714},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5910528302192688},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5494292974472046},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5355626940727234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.533820629119873},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.527588427066803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4918398857116699},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4908373951911926},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4284508228302002},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4113563299179077},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4084276854991913},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/itc44778.2020.9325225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itc44778.2020.9325225","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Test Conference (ITC)","raw_type":"proceedings-article"},{"id":"mag:3194855516","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202102239607100113","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320327708","display_name":"University of Texas at Dallas","ror":"https://ror.org/049emcs32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1563687030","https://openalex.org/W1967683336","https://openalex.org/W1970088130","https://openalex.org/W1980262437","https://openalex.org/W2091758325","https://openalex.org/W2107495488","https://openalex.org/W2110621778","https://openalex.org/W2122646361","https://openalex.org/W2130404536","https://openalex.org/W2132870739","https://openalex.org/W2134565911","https://openalex.org/W2139069061","https://openalex.org/W2162342991","https://openalex.org/W2168209902","https://openalex.org/W2171184034","https://openalex.org/W2296719434","https://openalex.org/W2398119937","https://openalex.org/W2569877732","https://openalex.org/W2599354622","https://openalex.org/W2737740651","https://openalex.org/W2763432333","https://openalex.org/W2786088545","https://openalex.org/W2803674491","https://openalex.org/W2803697594","https://openalex.org/W2910068345","https://openalex.org/W2954278343","https://openalex.org/W2963344330","https://openalex.org/W2963359731","https://openalex.org/W2963684275","https://openalex.org/W2963773039","https://openalex.org/W3120740533","https://openalex.org/W4243563432","https://openalex.org/W6712574313","https://openalex.org/W6741529945","https://openalex.org/W6748102297","https://openalex.org/W6751494907","https://openalex.org/W6751866786","https://openalex.org/W6758101687"],"related_works":["https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W3107369729","https://openalex.org/W4285233543","https://openalex.org/W4230838436","https://openalex.org/W4287241967","https://openalex.org/W3144173820","https://openalex.org/W165115930"],"abstract_inverted_index":{"Due":[0],"to":[1,12,49,88,99,105,111],"the":[2,27,59,68,82,113,118,123],"extreme":[3],"scarcity":[4],"of":[5,65,81,122],"customer":[6],"failure":[7],"data,":[8],"it":[9],"is":[10,93],"challenging":[11],"reliably":[13],"screen":[14],"out":[15],"those":[16],"rare":[17],"defects":[18],"within":[19],"a":[20,51,63,73,96],"high-dimensional":[21],"input":[22,91],"feature":[23],"space":[24],"formed":[25],"by":[26,57],"relevant":[28],"parametric":[29],"test":[30,45],"measurements.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,71,103],"study":[36],"several":[37],"unsupervised":[38,54],"learning":[39,55],"techniques":[40],"based":[41],"on":[42],"six":[43],"industrial":[44],"datasets,":[46],"and":[47,116,120],"propose":[48,104],"train":[50,72],"more":[52],"robust":[53],"model":[56],"self-labeling":[58,125],"training":[60],"data":[61,70,92,114],"via":[62],"set":[64],"transformations.":[66],"Using":[67],"labeled":[69],"multi-class":[74],"classifier":[75],"through":[76],"supervised":[77],"training.":[78],"The":[79],"goodness":[80],"multiclass":[83],"classification":[84],"decisions":[85],"with":[86],"respect":[87],"an":[89],"unseen":[90],"used":[94],"as":[95],"normality":[97],"score":[98],"defect":[100],"anomalies.":[101],"Furthermore,":[102],"use":[106],"reversible":[107],"information":[108,115],"lossless":[109],"transformations":[110],"retain":[112],"boost":[117],"performance":[119],"robustness":[121],"proposed":[124],"approach.":[126]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
