GA-SVM based feature selection and parameter optimization in hospitalization expense modeling

Abstract Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result.