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

  • Guvenir, H. Altay
  • Cakir, Murat

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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 21595.

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Date of creation: 2009
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Handle: RePEc:pra:mprapa:21595
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  1. G. Lanine & R. Vander Vennet, 2005. "Failure prediction in the Russian bank sector with logit and trait recognition models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/329, Ghent University, Faculty of Economics and Business Administration.
  2. Fukuda, Shin-ichi & Kasuya, Munehisa & Akashi, Kentaro, 2009. "Impaired bank health and default risk," Pacific-Basin Finance Journal, Elsevier, vol. 17(2), pages 145-162, April.
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