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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Consensus Approach for Combining Multiple Classifiers in Cost-Sensitive Bankruptcy Prediction
verfasst von : Ning Chen, Bernardete Ribeiro
Erschienen in: Adaptive and Natural Computing Algorithms
Verlag: Springer Berlin Heidelberg
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Bankruptcy prediction is an extremely important topic in the field of financial decision making. There has been a raising interest in studying more accurate predictive models able to provide valuable early warning before the real business failure. Recent researches suggested using the consensus of multiple classifiers for boosting the prediction performance. Yet rarely the cost of misclassification errors is considered in the literature of consensus decision making. In this paper we investigate the performance of classifier ensembles for cost-sensitive bankruptcy prediction. The selection of ensemble members is based on individual performance and pairwise diversity of classifiers. The experimental results on a real world database of French companies show that by selecting appropriate base classifiers the ensemble learning substantially improves the performance of cost-sensitive bankruptcy prediction.