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The Scientific World Journal
Volume 2014, Article ID 968712, 9 pages
http://dx.doi.org/10.1155/2014/968712
Research Article

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

1Department of Accounting Information, National Taipei University of Business, 321 Jinan Road, Section 1, Taipei 10051, Taiwan
2Department of Business Administration, National Taipei University, No. 67, Section 3, Ming-shen East Road, Taipei 10478, Taiwan

Received 13 May 2014; Revised 22 August 2014; Accepted 23 August 2014; Published 11 September 2014

Academic Editor: Shifei Ding

Copyright © 2014 Suduan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [7 citations]

The following is the list of published articles that have cited the current article.

  • Unknown, Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis, and Kyriakos Sgarbas, “Semi-supervised forecasting of fraudulent financial statements,” Proceedings of the 20th Pan-Hellenic Conference on Informatics - PCI '16, pp. 1–6, . View at Publisher · View at Google Scholar
  • Stamatis Karlos, Sotiris Kotsiantis, Nikos Fazakis, and Kyrgiakos Sgarbas, “Effectiveness of semi-supervised learning in bankruptcy prediction,” 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–6, . View at Publisher · View at Google Scholar
  • Yeung-Ja James Goo, Der-Jang Chi, and Zong-De Shen, “Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques,” Springerplus, vol. 5, 2016. View at Publisher · View at Google Scholar
  • Sachin Kamley, Thakur, and Shailesh Jaloree, “Performance forecasting of share market using machine learning techniques: A review,” International Journal of Electrical and Computer Engineering, vol. 6, no. 6, pp. 3196–3204, 2016. View at Publisher · View at Google Scholar
  • Petr Hajek, and Roberto Henriques, “Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods,” Knowledge-Based Systems, vol. 128, pp. 139–152, 2017. View at Publisher · View at Google Scholar
  • Stamatis Karlos, Sotiris Kotsiantis, Vassilis Tampakas, and Georgios Kostopoulos, “Using active learning methods for predicting fraudulent financial statements,” Communications in Computer and Information Science, vol. 744, pp. 351–362, 2017. View at Publisher · View at Google Scholar
  • Chyan-long Jan, “An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan,” Sustainability, vol. 10, no. 2, pp. 513, 2018. View at Publisher · View at Google Scholar