| Abstract: | Owing to the radical changing of Chinese economy, it is essential to build an effective financial distress prediction model. In this paper, we present a genetic algorithm (GA) approach for optimizing parameters of support vector machine (SVM). We validate the proposed model on datasets of Chinese high-tech manufacturing industry. Experimental results reveal that the proposed GA-SVM model can compare to and even outperform other exiting classifiers. Compared to grid-search algorithm, the proposed GA-based takes less time to optimize SVM parameter without degrading the prediction accuracy of SVM. |
