Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach

Although Asia is at the forefront of global economic growth, its investment environment is very risky and uncertain. Credit ratings are objective opinions about credit worthiness, investment risk, and default probabilities of issues or issuers. To classify credit ratings, analyze their determinants, and provide meaningful decision rules for interested parties, this work proposes an integrated procedure. First, this work adopts an integrated feature-selection approach to select key attributes, and then adopts an objective cumulative probability distribution approach (CPDA) to partition selected condition attributes by applying rough sets local-discretization cuts. This work then applies the rough sets LEM2 algorithm to generate a comprehensible set of decision rules. Finally, this work utilizes a rule filter to eliminate rules with poor support and thereby improve rule quality. The experimental focus was the Asian banking industry. Data were retrieved from a BankScope database that covers 1327 Asian banks. Experimental results demonstrate that the proposed procedure is an effective method of removing irrelevant attributes and achieving increased accuracy, providing a knowledge-based system for classification of rules for solving credit-rating problems encountered by banks, thereby benefiting interested parties.