{"id":"https://openalex.org/W2156507419","doi":"https://doi.org/10.1109/fuzzy.2009.5277180","title":"A preprocessing of outlier using KERNEL PCA and factor scores in regression model","display_name":"A preprocessing of outlier using KERNEL PCA and factor scores in regression model","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2156507419","doi":"https://doi.org/10.1109/fuzzy.2009.5277180","mag":"2156507419"},"language":"en","primary_location":{"id":"doi:10.1109/fuzzy.2009.5277180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzzy.2009.5277180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Fuzzy Systems","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/A5014690716","display_name":"Kyung-Whan Oh","orcid":null},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kyung-Whan Oh","raw_affiliation_strings":["Department of Computer Science and Engineering, Sogang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Sogang University, Seoul, South Korea","institution_ids":["https://openalex.org/I148751991"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016293435","display_name":"Sunghae Jun","orcid":"https://orcid.org/0000-0003-1961-0055"},"institutions":[{"id":"https://openalex.org/I1317225278","display_name":"Statistics Korea","ror":"https://ror.org/004g7jm05","country_code":"KR","type":"government","lineage":["https://openalex.org/I1317225278","https://openalex.org/I2801339556","https://openalex.org/I4210126005"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sunghae Jun","raw_affiliation_strings":["Department of Bioinformatics and Statistics, Chungbuk, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Bioinformatics and Statistics, Chungbuk, South Korea","institution_ids":["https://openalex.org/I1317225278"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114860739","display_name":"Yong-Jun Kim","orcid":"https://orcid.org/0000-0002-0159-2437"},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yong-Jun Kim","raw_affiliation_strings":["Department of Computer Science and Engineering, Sogang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Sogang University, Seoul, South Korea","institution_ids":["https://openalex.org/I148751991"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5014690716"],"corresponding_institution_ids":["https://openalex.org/I148751991"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.14337291,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2132","last_page":"2135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9966999888420105,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9854000210762024,"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/outlier","display_name":"Outlier","score":0.7801473140716553},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6685872077941895},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6526334881782532},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6373815536499023},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6105924844741821},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5767310261726379},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5712602734565735},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5696614384651184},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.5588633418083191},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5289261937141418},{"id":"https://openalex.org/keywords/local-outlier-factor","display_name":"Local outlier factor","score":0.5015621185302734},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4764321744441986},{"id":"https://openalex.org/keywords/principal-component-regression","display_name":"Principal component regression","score":0.47475817799568176},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.45263752341270447},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.43396860361099243},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.41957223415374756},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3652172088623047},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3611048758029938},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2375994324684143},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.21060407161712646},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08362695574760437}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7801473140716553},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6685872077941895},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6526334881782532},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6373815536499023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6105924844741821},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5767310261726379},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5712602734565735},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5696614384651184},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.5588633418083191},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5289261937141418},{"id":"https://openalex.org/C169029474","wikidata":"https://www.wikidata.org/wiki/Q387942","display_name":"Local outlier factor","level":3,"score":0.5015621185302734},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4764321744441986},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.47475817799568176},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.45263752341270447},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.43396860361099243},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.41957223415374756},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3652172088623047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3611048758029938},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2375994324684143},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.21060407161712646},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08362695574760437},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzzy.2009.5277180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzzy.2009.5277180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Fuzzy Systems","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":12,"referenced_works":["https://openalex.org/W208128215","https://openalex.org/W1565377632","https://openalex.org/W1914674302","https://openalex.org/W2103914106","https://openalex.org/W2112440119","https://openalex.org/W2124776405","https://openalex.org/W2140095548","https://openalex.org/W2140190241","https://openalex.org/W2482589566","https://openalex.org/W2898519191","https://openalex.org/W3143985493","https://openalex.org/W4299837303"],"related_works":["https://openalex.org/W2388959921","https://openalex.org/W5114324","https://openalex.org/W2803426353","https://openalex.org/W4318562271","https://openalex.org/W2890196891","https://openalex.org/W2375053148","https://openalex.org/W3078753841","https://openalex.org/W4318142392","https://openalex.org/W4385376269","https://openalex.org/W2097905957"],"abstract_inverted_index":{"Data":[0],"analysis":[1,9],"including":[2],"outlier":[3,13,37,74,110,115],"is":[4,62,122],"more":[5],"difficult":[6],"to":[7,17,51,107],"the":[8,19,23,28,36,42,53,58,73,109,112,125,141],"without":[10],"outlier.":[11],"The":[12,114],"has":[14,75],"a":[15,39,104],"chance":[16],"increase":[18],"misclassification":[20],"rate":[21],"and":[22,33,99,130],"variance":[24],"of":[25,57,117,127,137],"estimate":[26],"in":[27,41,68,111,148],"supervised":[29],"learning":[30],"like":[31],"classification":[32],"regression.":[34],"Also":[35],"becomes":[38],"cluster":[40],"clustering":[43,54],"as":[44],"unsupervised":[45],"learning.":[46],"So":[47],"we":[48,81,102],"are":[49],"hard":[50],"represent":[52],"result.":[55],"Because":[56],"previous":[59],"problems,":[60],"it":[61,85],"removed":[63],"generally":[64],"for":[65],"constructing":[66],"model":[67],"data":[69,88,120,146],"mining.":[70],"But":[71],"when":[72],"some":[76],"information":[77],"on":[78],"given":[79,118],"data,":[80],"must":[82],"not":[83],"remove":[84],"from":[86],"training":[87,119],"set.":[89],"In":[90],"this":[91],"paper,":[92],"using":[93,144],"kernel":[94,128],"PCA":[95,129],"(principal":[96],"component":[97],"analysis)":[98],"factor":[100,131],"scores,":[101],"propose":[103],"preprocessing":[105],"method":[106],"contain":[108],"modeling.":[113],"effect":[116],"set":[121],"reduced":[123],"by":[124,140],"values":[126],"scores.":[132],"We":[133],"verify":[134],"improved":[135],"performance":[136],"our":[138],"work":[139],"experimental":[142],"results":[143],"simulation":[145],"sets":[147],"regression":[149],"model.":[150]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}