Forecasting with neural networks
Information and Management
An IR-CBR Approach to Legal Indexing and Retrieval in Bankruptcy Law
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Computers and Operations Research
New Evolutionary Bankruptcy Forecasting Model Based on Genetic Algorithms and Neural Networks
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming
Expert Systems with Applications: An International Journal
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A genetic algorithm-based approach to cost-sensitive bankruptcy prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Comparative analysis of data mining methods for bankruptcy prediction
Decision Support Systems
Computers & Mathematics with Applications
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Agent based mobile negotiation for personalized pricing of last minute theatre tickets
Expert Systems with Applications: An International Journal
CART-based selection of bankruptcy predictors for the logit model
Expert Systems with Applications: An International Journal
Forecasting corporate bankruptcy with an ensemble of classifiers
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The Journal of Supercomputing
Hi-index | 12.06 |
This paper proposes a hybrid method for effective bankruptcy prediction, based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance with variable weight. Unlike the existing case-based reasoning methods using the Euclidean distance, we introduce the Mahalanobis distance in locating the nearest neighbors, which considers the covariance structure of variables in measuring the closeness. Since hundreds of financial ratio variables are available in analyzing credit management problems, the model performance is also affected by input variable selection strategies. Variables selected by the decision trees induction tend to have an interaction compared to those produced by the regression approaches. The Mahalanobis distance is a more true measure of proximity than the Euclidean distance when variables are correlated with each other. The experimental results indicate that the proposed approach outperforms some currently-in-use techniques.