{"id":"https://openalex.org/W3214652085","doi":"https://doi.org/10.1145/3480001.3480008","title":"A Data Slicing Method to Improve Machine Learning Model Accuracy in Bankruptcy Prediction","display_name":"A Data Slicing Method to Improve Machine Learning Model Accuracy in Bankruptcy Prediction","publication_year":2021,"publication_date":"2021-07-23","ids":{"openalex":"https://openalex.org/W3214652085","doi":"https://doi.org/10.1145/3480001.3480008","mag":"3214652085"},"language":"en","primary_location":{"id":"doi:10.1145/3480001.3480008","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3480001.3480008","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 5th International Conference on Deep Learning Technologies (ICDLT)","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/A5030133393","display_name":"Ziyuan Ye","orcid":"https://orcid.org/0000-0002-8370-7524"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziyuan Ye","raw_affiliation_strings":["Beijing University of Chemical Technology, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5030133393"],"corresponding_institution_ids":["https://openalex.org/I75390827"],"apc_list":null,"apc_paid":null,"fwci":0.2977,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64310895,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"32","last_page":"39"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9944000244140625,"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":0.9944000244140625,"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":0.9664999842643738,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9495999813079834,"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/slicing","display_name":"Slicing","score":0.8013688325881958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7476269006729126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6010937690734863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.582460880279541},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5612046718597412},{"id":"https://openalex.org/keywords/bankruptcy","display_name":"Bankruptcy","score":0.525327205657959},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3673866391181946},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.13896700739860535},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.07264798879623413}],"concepts":[{"id":"https://openalex.org/C2776190703","wikidata":"https://www.wikidata.org/wiki/Q488148","display_name":"Slicing","level":2,"score":0.8013688325881958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7476269006729126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6010937690734863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.582460880279541},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5612046718597412},{"id":"https://openalex.org/C504631918","wikidata":"https://www.wikidata.org/wiki/Q152074","display_name":"Bankruptcy","level":2,"score":0.525327205657959},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3673866391181946},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.13896700739860535},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.07264798879623413},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3480001.3480008","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3480001.3480008","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 5th International Conference on Deep Learning Technologies (ICDLT)","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/W2890297193","https://openalex.org/W2897596136","https://openalex.org/W2901769706","https://openalex.org/W2952574984","https://openalex.org/W2980996168","https://openalex.org/W2991061473","https://openalex.org/W2997325425","https://openalex.org/W2997806454","https://openalex.org/W3007817409","https://openalex.org/W3023632503","https://openalex.org/W3086659379","https://openalex.org/W3087296066"],"related_works":["https://openalex.org/W2393746923","https://openalex.org/W2972496411","https://openalex.org/W4239223006","https://openalex.org/W3033662781","https://openalex.org/W2137530048","https://openalex.org/W2360869927","https://openalex.org/W2050876785","https://openalex.org/W2074642116","https://openalex.org/W3164401656","https://openalex.org/W2392031372"],"abstract_inverted_index":{"High-accuracy":[0],"bankruptcy":[1,15,34,134],"prediction":[2,65],"has":[3],"been":[4],"important":[5],"to":[6,29,68,82,85,93,131,150,153,156],"investors":[7,141],"and":[8,19,56,60,78,100,114,125,142,164],"corporate":[9],"finance":[10],"officers":[11,146],"for":[12,136],"decades.":[13],"With":[14],"data":[16,46],"in":[17,33,71,98,133,160],"China":[18],"Poland":[20],"given,":[21],"this":[22,72],"paper":[23],"is":[24,118],"an":[25],"exploratory":[26,39],"study":[27],"attempting":[28],"aid":[30],"feature":[31],"engineering":[32],"predictions":[35,52],"through":[36],"a":[37,87],"new":[38],"method":[40],"we":[41],"call":[42],"\u201cData":[43],"Slicing.\u201d":[44],"Our":[45],"slicing":[47],"analysis":[48],"relies":[49],"on":[50,53,84],"making":[51],"carefully":[54],"selected":[55],"sliced":[57,63,89,107],"financial":[58],"datasets":[59],"measuring":[61],"each":[62],"dataset's":[64],"accuracy.":[66,138],"According":[67],"the":[69,74,79,109],"findings":[70],"research,":[73],"most":[75],"related":[76],"metric":[77],"best":[80],"variable":[81],"slice":[83],"get":[86],"predictable":[88],"dataset":[90],"turn":[91],"out":[92],"be":[94],"\u201cSolvency":[95],"Ratio\u201d":[96],"both":[97],"Chinese":[99],"Polish":[101],"data.":[102],"Simultaneously,":[103],"using":[104],"two":[105],"different":[106],"datasets,":[108],"accuracy":[110],"of":[111],"machine":[112],"learning":[113,116],"deep":[115],"methods":[117,128],"improved.":[119],"Support":[120],"Vector":[121],"Machine,":[122],"Neural":[123],"Networks":[124],"Random":[126],"Forest":[127],"are":[129,147],"suggested":[130],"use":[132],"detection":[135],"higher":[137],"In":[139],"summary,":[140],"other":[143],"risk":[144],"management":[145],"highly":[148],"recommended":[149],"pay":[151,157],"attention":[152],"firm's":[154],"ability":[155],"debts,":[158],"especially":[159],"their":[161],"valuation":[162],"attempts":[163],"forecasts.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-24T08:02:53.985720","created_date":"2025-10-10T00:00:00"}