Principal component case-based reasoning ensemble for business failure prediction

Case-based reasoning (CBR) has several advantages for business failure prediction (BFP), including ease of understanding, explanation, and implementation and the ability to make suggestions on how to avoid failure. We constructed a new ensemble method of CBR that we termed principal component CBR ensemble (PC-CBR-E): it, was intended to improve the predictive ability of CBR in BFP by integrating the feature selection methods in the representation level, a hybrid of principal component analysis with its two classical CBR algorithms at the modeling level and weighted majority voting at the ensemble level. We statistically validated our method by comparing it with other methods, including the best base model, multivariate discriminant analysis, logistic regression, and the two classical CBR algorithms. The results from a one-tailed significance test indicated that PC-CBR-E produced superior predictive performance in Chinese short-term and medium-term BFP.