关键词: 粒子群算法, 遗传算法, 支持向量机, 优化, 参数, 破产预测

Abstract: A method based on Support Vector Machine optimization by Particle Swarm Optimization and Genetic Algorithm is proposed for predicting bankruptcy. The proposed method integrates the merits of Particle Swarm Optimization, Genetic Algorithm and Support Vector Machine, which simultaneously searches optimal regularization parameter and kernel parameter of Support Vector Machine for optimal prediction model. A sample dataset comprised of bankruptcy and non-bankruptcy data derived from the UCI machine learning repository is used. The data are randomly read from the dataset and automatically preprocessed by normalization. A 7-fold cross-validation test is used to objectively evaluate the prediction results. The simulation results indicate that the proposed method can automatically and efficiently construct optimal Support Vector Machine. Compared with other methods, the proposed method has better generalization capability, faster learning speed and better bankruptcy prediction accuracy than the other methods.

Key words: Particle Swarm Optimization（PSO） algorithm, Genetic Algorithm（GA）, Support Vector Machine（SVM）, optimization, parameter, bankruptcy prediction