Acceso usuarios
This paper proposes a framework for an ensemble bankruptcy classifier that uses if�then rules to combine the outputs from a heterogeneous set of 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 form of if�then rules. We apply the approach to a balanced dataset and an imbalanced dataset. Both are composed of firms subject to financial distress and cited in the US Securities and Exchange Commission's Accounting and Auditing Enforcement Releases
