A hybrid approach of DEA, rough set and support vector machines for business failure prediction

The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. While the efficiency of a corporation’s management is generally acknowledged to be a key contributor to corporation’s bankrupt, it is usually excluded from early prediction models. The objective of the study is to use efficiency as predictive variables with a proposed novel model to integrate rough set theory (RST) with support vector machines (SVM) technique to increase the accuracy of the prediction of business failure. In the proposed method (RST–SVM), data envelopment analysis (DEA) is employed as a tool to evaluate the input/output efficiency. Furthermore, by RST approach, the redundant attributes in multi-attribute information table can be reduced, which showed that the number of independent variables was reduced with no information loss, is utilized as a preprocessor to improve business failure prediction capability by SVM. The effectiveness of the methodology was verified by experiments comparing back-propagation neural networks (BPN) approach with the hybrid approach (RST–BPN). The results shows that DEA do provide valuable information in business failure predictions and the proposed RST–SVM model provides better classification results than RST–BPN model, no matter when only considering financial ratios or the model including both financial ratios and DEA.