Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction

We examined the effectiveness of a hybrid method using a clustering technique and genetic algorithms based on the artificial neural networks model to balance the proportion between the minority class and majority class. The objective of this paper is to constitute the best suitable training dataset for both decreasing data imbalance and improving the classification accuracy. We extracted the properly balanced dataset composed of optimal or near-optimal instances for the artificial neural networks model. The main contribution of the proposed method is that we extract explorative knowledge based on recognition of the data structure and categorize instances through the clustering technique while performing simultaneous optimization for the artificial neural networks modeling. In addition, we can easily understand why the instances are selected by the rule-format knowledge representation increasing the expressive power of the criteria of selecting instances. The proposed method is successfully applied to the bankruptcy prediction problem using financial data for which the proportion of small- and medium-sized bankruptcy firms in the manufacturing industry is extremely small compared to that of non-bankruptcy firms.