{"id":"https://openalex.org/W3206149165","doi":"https://doi.org/10.1108/imds-10-2020-0603","title":"Forecasting the risk at infractions: an ensemble comparison of machine learning approach","display_name":"Forecasting the risk at infractions: an ensemble comparison of machine learning approach","publication_year":2021,"publication_date":"2021-10-10","ids":{"openalex":"https://openalex.org/W3206149165","doi":"https://doi.org/10.1108/imds-10-2020-0603","mag":"3206149165"},"language":"en","primary_location":{"id":"doi:10.1108/imds-10-2020-0603","is_oa":false,"landing_page_url":"https://doi.org/10.1108/imds-10-2020-0603","pdf_url":null,"source":{"id":"https://openalex.org/S37320504","display_name":"Industrial Management & Data Systems","issn_l":"0263-5577","issn":["0263-5577","1758-5783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Management &amp; Data 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/A5100440279","display_name":"Lei Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Li","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100694954","display_name":"Desheng Wu","orcid":"https://orcid.org/0000-0003-0999-952X"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Desheng Wu","raw_affiliation_strings":["School of Economics and Management, University of the Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, University of the Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100440279"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":1.1909,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.81752586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"122","issue":"1","first_page":"1","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12394","display_name":"Insurance and Financial Risk Management","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6180670261383057},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5258845686912537},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5042942762374878},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4990205764770508},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.48750898241996765},{"id":"https://openalex.org/keywords/originality","display_name":"Originality","score":0.48747944831848145},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.47279685735702515},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4562044143676758},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4556170105934143},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.446611613035202},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4160049855709076},{"id":"https://openalex.org/keywords/risk-analysis","display_name":"Risk analysis (engineering)","score":0.33031362295150757},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.13511264324188232},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.12496092915534973}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6180670261383057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5258845686912537},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5042942762374878},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4990205764770508},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.48750898241996765},{"id":"https://openalex.org/C2776950860","wikidata":"https://www.wikidata.org/wiki/Q2914681","display_name":"Originality","level":3,"score":0.48747944831848145},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.47279685735702515},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4562044143676758},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4556170105934143},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.446611613035202},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4160049855709076},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.33031362295150757},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13511264324188232},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12496092915534973},{"id":"https://openalex.org/C11012388","wikidata":"https://www.wikidata.org/wiki/Q170658","display_name":"Creativity","level":2,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/imds-10-2020-0603","is_oa":false,"landing_page_url":"https://doi.org/10.1108/imds-10-2020-0603","pdf_url":null,"source":{"id":"https://openalex.org/S37320504","display_name":"Industrial Management & Data Systems","issn_l":"0263-5577","issn":["0263-5577","1758-5783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Management &amp; Data Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W19790595","https://openalex.org/W222543348","https://openalex.org/W1498436455","https://openalex.org/W1944586112","https://openalex.org/W1976102130","https://openalex.org/W2015780725","https://openalex.org/W2016361990","https://openalex.org/W2048801439","https://openalex.org/W2064675550","https://openalex.org/W2068289534","https://openalex.org/W2083022762","https://openalex.org/W2086668606","https://openalex.org/W2111547563","https://openalex.org/W2119821739","https://openalex.org/W2124532504","https://openalex.org/W2131816657","https://openalex.org/W2136848157","https://openalex.org/W2161920802","https://openalex.org/W2270340764","https://openalex.org/W2284153934","https://openalex.org/W2296438605","https://openalex.org/W2300876166","https://openalex.org/W2342352817","https://openalex.org/W2409139449","https://openalex.org/W2424889563","https://openalex.org/W2610250061","https://openalex.org/W2624385633","https://openalex.org/W2811103148","https://openalex.org/W2900743306","https://openalex.org/W2903855372","https://openalex.org/W2911455822","https://openalex.org/W2911964244","https://openalex.org/W2919115771","https://openalex.org/W2940603351","https://openalex.org/W3039661298","https://openalex.org/W3122224265","https://openalex.org/W3122987467","https://openalex.org/W3124532889","https://openalex.org/W3124776508","https://openalex.org/W3150483408","https://openalex.org/W3160410628","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W2593155302","https://openalex.org/W2041415459","https://openalex.org/W2072812638","https://openalex.org/W3126095231","https://openalex.org/W2352855287","https://openalex.org/W2378906650","https://openalex.org/W4313459160","https://openalex.org/W4367331014","https://openalex.org/W3160122104","https://openalex.org/W4388681644"],"abstract_inverted_index":{"Purpose":[0],"The":[1,51,103,133,163],"infraction":[2,191],"of":[3,7,19,35,110,121,155,172,188,192,200],"securities":[4,193],"regulations":[5,194],"(ISRs)":[6,61],"listed":[8,36,174],"firms":[9],"in":[10,48],"their":[11],"day-to-day":[12],"operations":[13],"and":[14,46,65,74,92,158,209],"management":[15],"has":[16,142],"become":[17],"one":[18],"common":[20],"problems.":[21],"This":[22,196,223],"paper":[23],"proposed":[24,53,111],"several":[25],"machine":[26],"learning":[27],"approaches":[28],"to":[29,38,148,168,179,227],"forecast":[30],"the":[31,59,114,122,156,170,186,189,198,220,228],"risk":[32],"at":[33,177],"infractions":[34,60,116,154],"corporates":[37],"solve":[39],"financial":[40],"problems":[41,171],"that":[42,107],"are":[43],"not":[44,210],"effective":[45],"precise":[47],"supervision.":[49],"Design/methodology/approach":[50],"overall":[52],"research":[54,104],"framework":[55],"designed":[56],"for":[57,129],"forecasting":[58],"include":[62],"data":[63,69,160,203],"collection":[64],"cleaning,":[66],"feature":[67],"engineering,":[68],"split,":[70],"prediction":[71,100,108],"approach":[72],"application":[73],"model":[75],"performance":[76,109],"evaluation.":[77],"We":[78],"select":[79],"Logistic":[80],"Regression,":[81],"Na\u00efve":[82],"Bayes,":[83],"Random":[84],"Forest,":[85],"Support":[86],"Vector":[87],"Machines,":[88],"Artificial":[89],"Neural":[90],"Network":[91],"Long":[93],"Short-Term":[94],"Memory":[95],"Networks":[96],"(LSTMs)":[97],"as":[98],"ISRs":[99,123,176],"models.":[101],"Findings":[102],"results":[105,134,184],"show":[106],"models":[112,208,218],"with":[113],"prior":[115,190],"provides":[117],"a":[118,140,180],"significant":[119],"improvement":[120],"than":[124],"those":[125],"without":[126],"prior,":[127],"especially":[128],"large":[130,159],"sample":[131],"set.":[132],"also":[135,225],"indicate":[136],"when":[137,205],"judging":[138],"whether":[139],"company":[141],"infractions,":[143],"we":[144],"should":[145],"pay":[146],"attention":[147],"novel":[149],"artificial":[150],"intelligence":[151],"methods,":[152],"previous":[153],"company,":[157],"sets.":[161],"Originality/value":[162],"findings":[164],"could":[165],"be":[166],"utilized":[167],"address":[169],"identifying":[173],"corporates'":[175],"hand":[178],"certain":[181],"degree.":[182],"Overall,":[183],"elucidate":[185],"value":[187],"(ISRs).":[195],"shows":[197],"importance":[199],"including":[201],"more":[202,216],"sources":[204],"constructing":[206],"distress":[207],"only":[211],"focus":[212],"on":[213,219],"building":[214],"increasingly":[215],"complex":[217],"same":[221],"data.":[222],"is":[224],"beneficial":[226],"regulatory":[229],"authorities.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
