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  • 标题:KNOWLEDGE STRUCTURE FOR FRAUDULENT FIRM CLASSIFICATION APPROACHES
  • 本地全文:下载
  • 作者:Dr.G.Ayyappan ; Dr.A.Kumaravel
  • 期刊名称:Indian Journal of Computer Science and Engineering
  • 印刷版ISSN:2231-3850
  • 电子版ISSN:0976-5166
  • 出版年度:2019
  • 卷号:10
  • 期号:4
  • 页码:89-96
  • DOI:10.21817/indjcse/2019/v10i4/191004008
  • 出版社:Engg Journals Publications
  • 摘要:Machine learning gives most impact in developing the qualities of an audit work in the futuredue to enormous development of financial fraud. Here, seven hundred and seventy seven firm’s data infourteen different sectors are collected in this research study. This work proposed number ofclassification models are estimated in terms of their accuracies and time taken to build the models. Theresults of Trees and Rules are demonstrating an accuracy of high level for suspicious firm classification.This paper focuses improving the worth of an audit work by implementing a machine learningalgorithms.
  • 关键词:J48; Fraudulent Firm; Audit data; Machine Learning
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