Feature selection and parameter optimization for support vector machines

Research highlights? A genetic algorithm with feature chromosomes (GAFC) is proposed. ? The asymptotic behaviors of support vector machines (SVM) are fused with GA. ? The GAFC has not only the search ability of GA, but also has the search ability of feature chromosomes. ? The GAFC obtained good performances by optimizing feature subset and parameters of SVM simultaneously. Support vector machines (SVM) are an emerging data classification technique with many diverse applications. The feature subset selection, along with the parameter setting in the SVM training procedure significantly influences the classification accuracy. In this paper, the asymptotic behaviors of support vector machines are fused with genetic algorithm (GA) and the feature chromosomes are generated, which thereby directs the search of genetic algorithm to the straight line of optimal generalization error in the superparameter space. On this basis, a new approach based on genetic algorithm with feature chromosomes, termed GA with feature chromosomes, is proposed to simultaneously optimize the feature subset and the parameters for SVM.To evaluate the proposed approach, the experiment adopts several real world datasets from the UCI database and from the Benchmark database. Compared with the GA without feature chromosomes, the grid search, and other approaches, the proposed approach not only has higher classification accuracy and smaller feature subsets, but also has fewer processing time.