An improve feature selection algorithm for defect detection of glass bottles

Abstract

Defect detection is an effective technology to guarantee the quality of the products. In this paper, the defect detection of glass bottles is taken as a classification problem. Features abstracted from the knocking sound signals of the glass bottles are used as the input of the classifier. Because the detection of bottles usually should be done in real time, feature selection which could reduce the dimensionality of data and simplify the learned model has become an important technology in this work. Based on our previous proposed algorithm named Shuffled Frog Leaping Algorithm-Improved Minimal Redundancy Maximal Relevance(SFLA-ImRMR), this paper focuses on improving the classification performance and proposes an improved feature selection algorithm. In the proposed algorithm, feature selection is combined with the training of the classifier, the wrapper approach is used to evaluate the selected features, Shuffled Frog Leaping Algorithm(SFLA) is used as the search algorithm, and BP Neural Network(BPNN) is used as the classifier. The proposed algorithm is named SFLA-ImRMR-BP and tested in the data sets built by the knocking sound signals of the glass bottles. Compared with SFLA-ImRMR and some other feature selection algorithms based on Evolutionary Computation(EC), features selected by SFLA-ImRMR-BP can achieve the highest classification performance.