The Comparative of Attribute Selection Techniques between CFS and Consistency by Using ANFIS for Thai Enterprises Bankruptcy Prediction

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Kulthon Kasemsan
Wonlop Buachoom


- This paper presents the comparison of attribute selection techniques between CFS and Consistency for seeking better technique which could appropriately associate with ANFIS. Better model will be used for predicting business bankruptcy in Thai enterprises. According the objective of this study, there are two prediction models, CFS-ANFIS and Consistency-ANFIS. Type 1 error from estimation, which effect to stakeholders’ decision making, is used for consider each model. The result indicates that estimation error rates obtained from CFS-ANFIS are lower than the error rates obtained from Consistency-ANFIS.


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How to Cite
K. Kasemsan and W. Buachoom, “The Comparative of Attribute Selection Techniques between CFS and Consistency by Using ANFIS for Thai Enterprises Bankruptcy Prediction”, JIST, vol. 1, no. 1, pp. 9-14, Jun. 2010.
Research Article: Soft Computing (Detail in Scope of Journal)


1. Abdelwahed, T. and Amir E.M. “ New Evolutionary Bankruptcy Forecasting Model Based on Genetic Algorithms and Neural Network”. Proceedings of the 17th IEEE International Conference on Tool with Artificial Intelligence, 2005.

2. Altman, Edward I. “Financial Ratios Discriminant Analysis and The Prediction of Corporate Bankruptcy”. The journal of Finance. 4 (1968) : 589-609.

3. Huang Fu-yuan. “A Genetic Fuzzy Neural Network for Bankruptcy Prediction in Chinese Corporations”. Proceeding of the 2008 International Conference on Risk Management & Engineering Management. IEEE Computer Society, 2008.

4. Tong Srikhacha. “Short-Term Prediction in Stock Price Using Hybrid Optimized Recursive Slope Filtering, Adaptive Moving Approach and Neurofuzzy Adaptive Learning”. PhD thesis, Department of Information Technology, King Mongkut’s Institute of Technology North Bangkok,Thailand, 2007.

5. Borges, Helyane B. and Nievola, Julio C. “Attribute Selection Methods Comparison for Classification of Diffuse Large B-Cell Lymphoma”. Proceedings of the Fourth International Conference on Machine Learning and Applications. IEEE Computer Society, 2005.

6. Hall, Mark A. and Holmes, Geoffrey. “Benchmarking Attribute Selection Techniques for Discrete Class Data Mining”. IEEE Trans on Knowledge and Data Engineering. 15 (2003) : 1437-1447.

7. Sukontip Wongpun and Anongnart Srivihok. “Comparison of Attribute Selection Techniques and Algorithms in Classifying Bad Behaviors of Vocational Education Students”. Proceedings of 2008 Second IEEE International Conference on Digital Ecosystems and Technologies. IEEE Computer Society, 2008.

8. Sukontip Wongpun. “Comparison of Attribute Selection Techniques and Algorithms in Classifying Mistaken Behaviors of Vocational Education Students”. Master of Science thesis, Department of Computer Science, Kasetsart University, Thailand, 2008.

9. Jang, J. S., Sun, C.T. and Mizutani, E. Neuro-Fuzzy and Soft Computing : A Computational Approach to Learning and Machine Intelligence. n.p. : Prentice-Hall, 1997.

10. Jang, S. R. “ANFIS: Adaptive-Network-Based Fuzzy Inference System”. IEEE Trans. System, Man and Cybernetic. 23 (1993) : 665-684.

11. Gibson, Charles H. Financial Reporting and Analysis. Ohio : Thomson South-western, 2007.

12. Stickney, Clyde P. Financial Reporting and Statement Analysis.Texas:The Dryden Press, 1996.

13. Walsh Ciaran. Key Management Ratios The Clearest Guide to The Critical Numbers That Drive Your Business. Pearson Education, 2006.