CSO-based feature selection and parameter optimization for support vector machine

This research constructs the CSO+SVM model for data classification through integrating cat swam optimization into SVM classifier. There are two factors (i.e. feature selection and parameter determination) of classification problems will mainly discuss in this study. The objectives of feature selection are to reduce number of features and remove irrelevant, noisy and redundant data. Besides, the parameter optimization for training can improve classification performance. Hence, the optimal feature subset and kernel parameter are applied to SVM classifier for reducing the computational time in an acceptable classification accuracy. Furthermore, the classification accuracy is increased. The different classes and types in UCI machine learning repository is used to evaluate the classification accuracy of the proposed CSO+SVM and GA+SVM methods.. Experimental results show the effectiveness of the proposed CSO+SVM method for solving data classification problems.