Designing an If-Then Rules Based Ensemble of Heterogeneous Bankruptcy Classifiers: A Genetic Algorithm Approach

Abstract

We propose a framework for an ensemble bankruptcy classifier that uses if-then rules to combine the outputs from a heterogeneous set classifiers. A genetic algorithm (GA) induces the rules using an asymmetric, cost-sensitive fitness function that includes accuracy and misclassification costs. The GA-based ensemble classifier outperforms individual classifiers and ensemble classifiers generated by other methods. The results of the classifier are in the in the form of if-then rules. We apply the approach to a balanced data set and an imbalanced data set. Both are composed of firms subject to financial distress and cited in the U.S. Securities and Exchange Commission's (SEC) Accounting and Auditing Enforcement Releases (AAER).

Keywords: Ensemble, Genetic Algorithm, If then Rule Based, Bankruptcy Classifications, Asymmetric Cost Function, SEC, AAER, Accounting and Political Economy