Improving the classification accuracy of the weld defect by chaos-search-based feature selection

Abstract

To improve the classification accuracy of the weld defect, we suggest a chaos-search-based feature selection method (CSFS). We regard the selection of the features as an optimisation problem in which the classification accuracy is taken as the object function. Firstly, n feature candidates are arranged in a predefined order and then an n-bit binary number is constructed, in which each bit represents whether the corresponding feature is selected or not. Secondly, the binary number is converted to a chaotic variable, and then chaos search is employed to find the feature subset. Thirdly, classifiers are used to evaluate the performance of the feature subset found. Three classifiers are employed and the performance of CSFS is compared with that of a genetic-algorithm-based feature selection method (GAFS). The experimental result shows that the selection of the features can improve the accuracy of the classification of the weld defect and that the best feature subset is classifier dependent. The average classification accuracy of the weld defect when using the feature subset obtained by CSFS is 2.22% higher than that obtained by CAFS; meanwhile, the computation time is cut by 7.99%.