{"id":"https://openalex.org/W2748097363","doi":"https://doi.org/10.4018/ijdwm.2017100104","title":"When Should We Ignore Examples with Missing Values?","display_name":"When Should We Ignore Examples with Missing Values?","publication_year":2017,"publication_date":"2017-08-15","ids":{"openalex":"https://openalex.org/W2748097363","doi":"https://doi.org/10.4018/ijdwm.2017100104","mag":"2748097363"},"language":"en","primary_location":{"id":"doi:10.4018/ijdwm.2017100104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijdwm.2017100104","pdf_url":null,"source":{"id":"https://openalex.org/S53932126","display_name":"International Journal of Data Warehousing and Mining","issn_l":"1548-3924","issn":["1548-3924","1548-3932"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Warehousing and Mining","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/A5101926937","display_name":"Wei-Chao Lin","orcid":"https://orcid.org/0000-0001-9640-0233"},"institutions":[{"id":"https://openalex.org/I143578492","display_name":"Asia University","ror":"https://ror.org/038a1tp19","country_code":"TW","type":"education","lineage":["https://openalex.org/I143578492"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wei-Chao Lin","raw_affiliation_strings":["Asia University, Taichung, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Asia University, Taichung, Taiwan","institution_ids":["https://openalex.org/I143578492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111973053","display_name":"Shih-Wen Ke","orcid":null},"institutions":[{"id":"https://openalex.org/I151221077","display_name":"Chung Yuan Christian University","ror":"https://ror.org/02w8ws377","country_code":"TW","type":"education","lineage":["https://openalex.org/I151221077"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shih-Wen Ke","raw_affiliation_strings":["Chung Yuan Christian University, Taoyuan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung Yuan Christian University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I151221077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045234607","display_name":"Chih\u2010Fong Tsai","orcid":"https://orcid.org/0000-0002-5991-2253"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Fong Tsai","raw_affiliation_strings":["Department of Information Management, National Central University, Jhongli, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Management, National Central University, Jhongli, Taiwan","institution_ids":["https://openalex.org/I22265921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.496,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74348492,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"13","issue":"4","first_page":"53","last_page":"63"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9876999855041504,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/missing-data","display_name":"Missing data","score":0.8770397901535034},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.8678311705589294},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7708511352539062},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6822700500488281},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6315694451332092},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.42327946424484253},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2928500175476074}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8770397901535034},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8678311705589294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7708511352539062},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6822700500488281},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6315694451332092},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.42327946424484253},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2928500175476074},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/ijdwm.2017100104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijdwm.2017100104","pdf_url":null,"source":{"id":"https://openalex.org/S53932126","display_name":"International Journal of Data Warehousing and Mining","issn_l":"1548-3924","issn":["1548-3924","1548-3932"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Warehousing and Mining","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:jdwm00:v:13:y:2017:i:4:p:53-63","is_oa":false,"landing_page_url":"https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.2017100104","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W105168457","https://openalex.org/W1493193319","https://openalex.org/W1574627325","https://openalex.org/W1598553907","https://openalex.org/W1776383492","https://openalex.org/W1841820628","https://openalex.org/W1983479840","https://openalex.org/W1986691668","https://openalex.org/W1988176704","https://openalex.org/W1995880999","https://openalex.org/W2031459800","https://openalex.org/W2043851564","https://openalex.org/W2044758663","https://openalex.org/W2132549764","https://openalex.org/W2136691316","https://openalex.org/W2146332392","https://openalex.org/W2159798994","https://openalex.org/W2210833864","https://openalex.org/W4245588985"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W2753779043"],"abstract_inverted_index":{"In":[0,94,111],"practice,":[1],"the":[2,29,43,103,127,132,139,153,156],"dataset":[3],"collected":[4],"from":[5],"data":[6,24,52,67],"mining":[7],"usually":[8],"contains":[9],"some":[10,77],"missing":[11,26,30,54,129],"values.":[12],"It":[13],"is":[14,32,40,72,115],"common":[15],"practice":[16],"to":[17,41,100],"perform":[18],"case":[19,85,145],"deletion":[20,86,120,146],"by":[21,106,144],"ignoring":[22],"those":[23],"with":[25,53,119,126],"values":[27],"if":[28],"rate":[31],"certainly":[33],"small.":[34],"The":[35,135],"aim":[36],"of":[37,60,65,155],"this":[38],"paper":[39],"answer":[42],"following":[44],"question:":[45],"When":[46],"should":[47],"one":[48],"directly":[49],"ignore":[50],"sampled":[51],"values?":[55],"By":[56],"using":[57],"different":[58,88,124],"types":[59],"datasets":[61,89,125],"having":[62],"various":[63],"numbers":[64],"attributes,":[66],"samples,":[68],"and":[69,121,131,147],"classes,":[70],"it":[71],"found":[73],"that":[74,80,138],"there":[75],"are":[76,98,149],"specific":[78],"patterns":[79,97],"can":[81],"be":[82],"considered":[83],"for":[84],"over":[87,123],"without":[90],"significant":[91],"performance":[92,141],"degradation.":[93],"particular,":[95],"these":[96],"extracted":[99,157],"act":[101],"as":[102],"decision":[104,108,133,158],"rules":[105],"a":[107,113],"tree":[109],"model.":[110],"addition,":[112],"comparison":[114],"made":[116],"between":[117],"cases":[118],"imputation":[122,148],"allowed":[128],"rates":[130],"rules.":[134,159],"results":[136,142],"show":[137],"classification":[140],"obtained":[143],"similar,":[150],"which":[151],"demonstrates":[152],"reliability":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T06:19:23.411458","created_date":"2025-10-10T00:00:00"}