Title: Feature selection and fuzzy rule-based classifier applied to bankruptcy prediction in banks

Authors: Devulapalli Karthik Chandra, Vadlamani Ravi

Addresses: Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad 500 057, Andhra Pradesh, India. ' Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad 500 057, Andhra Pradesh, India

Abstract: We propose a fuzzy |if-then| rule-based expert system preceded by feature selection for predicting bankruptcy in Turkish, Spanish and the UK banks. The system comprises three phases: feature selection, rule generation and optimisation. Feature selection reduces the number of rule antecedents, thereby enhancing human comprehensibility without sacrificing accuracy. Top five features are selected using wavelet neural network-based algorithm, t-statistic and f-statistic. Then, the reduced set of features is fed to a fuzzy rule-based classifier (FRBC) that generates rules and the third phase optimises the rule base. Tenfold cross-validation is performed throughout. The frequency of the presence of a rule in each fold decides the common rule base. The experiments are conducted for 2-4 partitions and area under receiver operating characteristic curve criterion selects the optimal partition and the best feature selection algorithm. Results indicate the effectiveness of FRBC with feature selection in predicting bank bankruptcy.

Keywords: bankruptcy prediction; feature selection; FRBC; fuzzy rule-based classifiers; MTA; modified threshold accepting algorithm; rule generation; WNN; wavelet neural networks; expert systems; bank bankruptcy; Turkey; Spain; UK; United Kingdom; fuzzy logic.

DOI: 10.1504/IJIDS.2009.027756

International Journal of Information and Decision Sciences, 2009 Vol.1 No.4, pp.343 - 365

Published online: 10 Aug 2009 *

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