The feature selection and bias classification of magnetic tile surface defect

In order to improve the accuracy and reduce the prediction time of detection of magnetic tile surface defect,a method of the feature selection and the bias classification is proposed.While offline training,the subgraphs which are generated from the transformation by Gabor filters are fused.Then the texture features of the pictures are extracted.The Relief algorithm is improved to extract the feature subset which have a strong correlation with category and remove redundant features.In order to decrease the miss rate of defective magnetic tile,the bias classification is performed before used LSSVM to predict the categories.It is proved that the proposed method can achieve about 99.09%as the accuracy rate of the defect magnet and the overall accuracy rate is about 96.89%.Compared with the original method,the online prediction only costs 67.4ms which decreased by nearly 1/4.