{"id":"https://openalex.org/W4220808861","doi":"https://doi.org/10.3233/jifs-212096","title":"LDAAD: An effective label de-noising approach for anomaly detection","display_name":"LDAAD: An effective label de-noising approach for anomaly detection","publication_year":2022,"publication_date":"2022-03-29","ids":{"openalex":"https://openalex.org/W4220808861","doi":"https://doi.org/10.3233/jifs-212096"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-212096","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-212096","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-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/A5009221204","display_name":"Lujia Pan","orcid":"https://orcid.org/0000-0002-8988-4740"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]},{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lujia Pan","raw_affiliation_strings":["NSKeyLab, Xi\u2019an Jiaotong University, Xi\u2019an, China","Noah\u2019s Ark Lab, Huawei Technologies, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NSKeyLab, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Noah\u2019s Ark Lab, Huawei Technologies, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090313897","display_name":"Marcus Kalander","orcid":null},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Marcus Kalander","raw_affiliation_strings":["Noah\u2019s Ark Lab, Huawei Technologies, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Noah\u2019s Ark Lab, Huawei Technologies, Hong Kong","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022240408","display_name":"Pinghui Wang","orcid":"https://orcid.org/0000-0001-5779-6108"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pinghui Wang","raw_affiliation_strings":["NSKeyLab, Xi\u2019an Jiaotong University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NSKeyLab, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090313897"],"corresponding_institution_ids":["https://openalex.org/I2250955327"],"apc_list":null,"apc_paid":null,"fwci":0.1312,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49165545,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"42","issue":"6","first_page":"5627","last_page":"5637"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9991999864578247,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9991999864578247,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9988999962806702,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9847000241279602,"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/computer-science","display_name":"Computer science","score":0.8269566297531128},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.763890266418457},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7603356838226318},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6228196620941162},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5185204744338989},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4911138713359833},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4738215208053589},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.46337902545928955},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.46150141954421997},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45424944162368774},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45262476801872253},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45176202058792114},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4137977361679077},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.41300612688064575},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09804275631904602}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8269566297531128},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.763890266418457},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7603356838226318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6228196620941162},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5185204744338989},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4911138713359833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4738215208053589},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.46337902545928955},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.46150141954421997},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45424944162368774},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45262476801872253},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45176202058792114},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4137977361679077},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.41300612688064575},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09804275631904602},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-212096","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-212096","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.4699999988079071,"display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1975128126","https://openalex.org/W2127883478","https://openalex.org/W2167460663","https://openalex.org/W2172852798","https://openalex.org/W2615509641","https://openalex.org/W2911964244","https://openalex.org/W2924453407","https://openalex.org/W2945594226","https://openalex.org/W2959716986","https://openalex.org/W2984970420","https://openalex.org/W6679102953"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W4388103534","https://openalex.org/W4312531262","https://openalex.org/W4318142952","https://openalex.org/W2072070946","https://openalex.org/W2149903055","https://openalex.org/W4308235887","https://openalex.org/W2168489430","https://openalex.org/W180869904"],"abstract_inverted_index":{"Classification":[0],"algorithms":[1],"are":[2,25,41,137],"widely":[3],"applied":[4],"to":[5,18,33,53,60,107,140,149,160],"predict":[6],"failures":[7],"and":[8,23,98,114,124,148,177],"detect":[9],"anomalies":[10],"in":[11,35,44,56,75,144],"various":[12],"application":[13],"areas.":[14],"It":[15],"is":[16,31,213],"common":[17],"assume":[19],"that":[20,94,179,184],"the":[21,36,45,109,132,142,145,151,157,186,197],"data":[22,111],"labels":[24,43],"correct":[26],"when":[27,209],"training,":[28],"but":[29],"this":[30,79],"challenging":[32],"guarantee":[34],"real":[37],"world.":[38],"If":[39],"there":[40],"erroneous":[42],"training":[46,110],"data,":[47],"a":[48,84,162,192,205],"model":[49],"can":[50],"easily":[51],"overfit":[52],"these,":[54],"resulting":[55],"poor":[57],"performance.":[58],"How":[59],"handle":[61],"label":[62,73,89,211],"noise":[63,74,202,212],"has":[64],"been":[65],"previously":[66],"researched,":[67],"however,":[68],"few":[69],"works":[70],"focus":[71],"on":[72,129,173],"anomaly":[76,92,105],"detection.":[77],"In":[78],"work,":[80],"we":[81,103,155],"propose":[82],"LDAAD,":[83],"novel":[85],"algorithm":[86],"framework":[87],"for":[88,91,200],"de-noising":[90],"detection":[93,106],"combines":[95],"unsupervised":[96],"learning":[97,100],"semi-supervised":[99],"methods.":[101,188],"Specifically,":[102,189],"apply":[104],"partition":[108],"into":[112],"low-risk":[113,133,152],"high-risk":[115,146],"sets.":[116],"We":[117,170],"subsequently":[118],"build":[119],"upon":[120],"ideas":[121],"from":[122],"cross-validation":[123],"train":[125],"multiple":[126,174],"classification":[127],"models":[128,136],"segments":[130],"of":[131],"data.":[134],"The":[135],"used":[138],"both":[139],"relabel":[141],"samples":[143],"set":[147,165],"filter":[150],"samples.":[153],"Finally,":[154],"merge":[156],"two":[158],"sets":[159],"obtain":[161],"final":[163],"sample":[164],"with":[166],"more":[167],"confident":[168],"labels.":[169],"evaluate":[171],"LDAAD":[172,180,190],"real-world":[175],"datasets":[176],"show":[178],"achieves":[181,191],"robust":[182],"results":[183],"outperform":[185],"benchmark":[187],"5%":[193],"accuracy":[194],"improvement":[195],"over":[196],"second-best":[198],"method":[199],"symmetric":[201],"while":[203],"having":[204],"minimal":[206],"detrimental":[207],"impact":[208],"no":[210],"present.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}