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 s...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 GAo 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.展开更多
基金Supported by the Cultivation Fund of the Key Scientific and Technical Innovation Project from Ministry of Education of China ( No.706024)the International Science Cooperation Foundation of Shanghai (No.061307041)the Excellent Youth Foundation ofShanghai (No.07A212)
文摘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 GAo 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.
