Bankruptcy Prediction by Genetic Ant Colony Algorithm
Corporate bankruptcy is a hot topic in economical research. Traditional methods cannot reach satisfying classification accuracy due to the high dimensional features. In this study, we proposed a novel method based on wrapper-based feature selection. Moreover, a novel genetic ant colony algorithm (GACA) was proposed as the search method, and the rule-based model was employed as the classifier. Stratified K-fold cross validation method was taken as the statistical resampling to reduce overfitting. Simulations take 1,000 runs of each algorithm on the dataset of 800 corporations during the period 2006-2008. The results of the training subset show that the GACA obtains 84.3% success rate, while GA obtains only 48.8% and ACA obtains 22.1% success rate. The results on test subset demonstrate that the mean misclassification error of GACA is only 7.79%, less than those of GA (19.31%) and ACA (23.89%). The average computation time of GACA is only 0.564s compared to the GA (1.203s) and ACA (1.109s).
Wenya Tian and Linli Xu
Y. D. Zhang and L. N. Wu, "Bankruptcy Prediction by Genetic Ant Colony Algorithm", Advanced Materials Research, Vol. 186, pp. 459-463, 2011