1. A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy
- Author
-
Yeong-Jia Goo, Chih-Hung Wu, Gwo-Hshiung Tzeng, and Wen-Chang Fang
- Subjects
Multivariate statistics ,Structured support vector machine ,business.industry ,Computer science ,Generalization ,Logit ,General Engineering ,Probit ,Machine learning ,computer.software_genre ,Computer Science Applications ,Support vector machine ,ComputingMethodologies_PATTERNRECOGNITION ,Artificial Intelligence ,Bankruptcy ,Ranking SVM ,Genetic algorithm ,Artificial intelligence ,Data mining ,business ,computer - Abstract
Two parameters, C and σ, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GA-SVM) model that can automatically determine the optimal parameters, C and σ, of SVM with the highest predictive accuracy and generalization ability simultaneously. This paper pioneered on employing a real-valued genetic algorithm (GA) to optimize the parameters of SVM for predicting bankruptcy. Additionally, the proposed GA-SVM model was tested on the prediction of financial crisis in Taiwan to compare the accuracy of the proposed GA-SVM model with that of other models in multivariate statistics (DA, logit, and probit) and artificial intelligence (NN and SVM). Experimental results show that the GA-SVM model performs the best predictive accuracy, implying that integrating the RGA with traditional SVM model is very successful.
- Published
- 2007