Neural Networks
Neural networks and the bias/variance dilemma
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Predicting financial activity with evolutionary fuzzy case-based reasoning
Expert Systems with Applications: An International Journal
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Ensemble with neural networks for bankruptcy prediction
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Switching class labels to generate classification ensembles
Pattern Recognition
A seasonal discrete grey forecasting model for fashion retailing
Knowledge-Based Systems
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Accurate prediction of corporate financial distress is very important for managers, creditors and investors to take correct measures to reduce loss. Many quantitative methods have been employed to develop empirical models for predicting corporate bankruptcy. However, there is so much information disclosed in the companies' financial statements, what information should be selected for building the empirical models with objective to maximize the predictive accuracy. In this study, more than 20 models based on six features ranking strategies are tested on North American companies and Chinese listed companies. The experimental results are helpful to develop financial models by choosing the proper quantitative methods and features selection strategy.