A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction

This paper proposes a hybrid method for effective bankruptcy prediction, based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance with variable weight. Unlike the existing case-based reasoning methods using the Euclidean distance, we introduce the Mahalanobis distance in locating the nearest neighbors, which considers the covariance structure of variables in measuring the closeness. Since hundreds of financial ratio variables are available in analyzing credit management problems, the model performance is also affected by input variable selection strategies. Variables selected by the decision trees induction tend to have an interaction compared to those produced by the regression approaches. The Mahalanobis distance is a more true measure of proximity than the Euclidean distance when variables are correlated with each other. The experimental results indicate that the proposed approach outperforms some currently-in-use techniques.