Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction

Abstract

Bankruptcy prediction and credit scoring are major problems in financial distress prediction. Studies have shown that prediction models can be made more effective by performing data preprocessing procedures. Moreover, classifier ensembles are likely to outperform single classifiers. Although feature selection, instance selection, and classifier ensembles are known to affect final prediction results, their combined effects on bankruptcy prediction and credit scoring problems have not been fully explored. This study compares the performance of three feature selection algorithms, three instance selection algorithms, four classification algorithms, and two ensemble learning techniques. The results obtained using five bankruptcy prediction and five credit scoring datasets indicate that by carefully considering the combination of these three factors, better prediction models can be developed than by considering only one related factor.