Voting Features based Classifier with Feature Construction and its Application to Predicting Financial Distress

Guvenir, H. Altay and Cakir, Murat (2009): Voting Features based Classifier with Feature Construction and its Application to Predicting Financial Distress.

Abstract

Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms.

• Item Type: MPRA Paper
• Language: English
• Keywords: Classification; Voting; Feature construction; Financial distress; Feature projections
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C69 - Other; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C88 - Other Computer Software; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access; G - Financial Economics > G3 - Corporate Finance and Governance > G33 - Bankruptcy ; Liquidation; G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages

References

Apea, C., & Sezibera, J. (2002). Some causes of bank failure: A case study of Ghana Co-operative Bank Ltd., International Accounting and Finance Master Thesis No. 2002:54, Göteborg University, Graduate Business School, School of Economics and Commercial Law, ISSN 1403-841X.

Asuncion, A. & Newman, D.J. (2007). UCI Machine learning repository <http://www.ics.uci.edu/~mlearn/MLRepository.html>. Irvine, CA: University of California, School of Information and Computer Science.

Bradshaw, G. L., Langley, P., & Simon, H. A. (1980). BACON.4: The discovery of intrinsic properties. In Proceedings of the third national conference of the canadian society for computational studies of intelligence (pp. 19–25).

Fukuda, S., Kasuya, M., & Akashi, K. (2008). Impaired bank health and default risk. Pacific-Basin Finance Journal. doi:10.1016/j.pacfin.2008.06.002.

Güvenir, H. A., Demiröz, G., & Ilter, N. (1998). Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artificial Intelligence in Medicine, 13(3), 147–165.

Güvenir, H. A., & Sirin, I. (1996). Classification by feature partitioning. Machine Learning, 23(1), 47–67.

Güvenir, H. A., Emeksiz, N., Ikizler, N., & Örmeci, N. (2004). Diagnosis of gastric carcinoma by classification on feature projections. Artificial Intelligence in Medicine, 31(3), 231–240.

Güvenir, H. A. (1998). A classification learning algorithm robust to irrelevant features, in artificial intelligence: Methodology, systems applications. In Fausto Giunchiglia (Ed.), Proceedings of AIMSA’98, LNAI 1480 (pp. 281–290). Sozopol, Bulgaria: Springer-Verlag.

Hanczar, B., Zucker, J-D., Henegar, C., & Saitta, L. (2007). Feature construction from synergic pairs to improve microarray-based classification. Bioinformatics, 23(21), 2866–2872.

Kim, C., & Choi, C. H. (2007). A discriminant analysis using composite features for classification problems. Pattern Recognition, 40(11), 2958–2966.

Lanine, G., & Vennet, R. V. (2006). Failure prediction in the Russian bank sector with logit and trait recognition models. Expert Systems with Applications, 30(3), 463–478.

Ng, G. S., Quek, C., & Jiang, H. (2008). FCMAC-EWS: A bank failure early warning system based on a novel localized pattern learning and semantically associative fuzzy neural network. Expert Systems with Applications, 34(2), 989–1003.

Piramuthu, S. (2004). Feature construction for reduction of tabular knowledge based systems. Information Sciences, 168(1–4), 201–215.

Tung, W. L., Quek, C., & Cheng, P. (2004). GenSo-EWS: A novel neural-fuzzy based early warning system for predicting bank failures. Neural Networks, 17(4), 567–587.

Utgoff, P. E. (2001). Feature construction for game playing. In Fuerenkranz & Kubat (Eds.), Machines that learn to play games (pp. 131–152). Nova Science Publishers.

Westernhagen, N., Harada, E., Nagata, T., Vale, B., et al. (2004). Bank failures in mature economies, Basel Committee on Banking Supervision Working Paper No. 13.

Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). San Francisco: Morgan Kaufmann.