Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Computers and Operations Research
Deciding the financial health of dot-coms using rough sets
Information and Management
Expert Systems with Applications: An International Journal
A boosting approach for corporate failure prediction
Applied Intelligence
Soft computing system for bank performance prediction
Applied Soft Computing
Mining competent case bases for case-based reasoning
Artificial Intelligence
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An association-based case reduction technique for case-based reasoning
Information Sciences: an International Journal
Financial distress early warning based on group decision making
Computers and Operations Research
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Majority voting combination of multiple case-based reasoning for financial distress prediction
Expert Systems with Applications: An International Journal
Adapting the CBA algorithm by means of intensity of implication
Information Sciences: an International Journal
Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction
Computers and Operations Research
Agent based mobile negotiation for personalized pricing of last minute theatre tickets
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
A real-time risk control and monitoring system for incident handling in wine storage
Expert Systems with Applications: An International Journal
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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.