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- W2760741696 abstract "This study explores the use of data mining methods to detect fraud for on e-ledgers through financial statements. For this purpose, data set were produced by rule-based control application using 72 sample e-ledger and error percentages were calculated and labeled. The financial statements created from the labeled e-ledgers were trained by different data mining methods on 9 distinguishing features. In the training process, Linear Regression, Artificial Neural Networks, K-Nearest Neighbor algorithm, Support Vector Machine, Decision Stump , M5P Tree, J48 Tree, Random Forest and Decision Table were used. The results obtained are compared and interpreted." @default.
- W2760741696 created "2017-10-06" @default.
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- W2760741696 date "2017-09-30" @default.
- W2760741696 modified "2023-09-27" @default.
- W2760741696 title "Fraud Detection on Financial Statements Using Data Mining Techniques" @default.
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- W2760741696 doi "https://doi.org/10.18201/ijisae.2017531428" @default.
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