As the classification of magnetic tile surface defect has a low efficiency, a bias classification of magnetic tile surface defect based on the Least Squares Support Vector Machine (LSSVM) is proposed. A Gabor filter group is constructed, which has 5 scales and 8 directions, and extracts the mean and variance of the 40 pictures which are generated from the transformation by Gabor filters as the features of the magnet. CSA is used for the initial optimization, and produces the initial parameters. Then the meticulous search using grid algorithm is done for the neighborhood of the initial parameters. In order to realize classification flipped to defect magnet, the SMOTE algorithms are improved. After the noise samples in original samples are removed, over-sampling between the accepted samples and boundary samples of defect magnet is done. It is proved that the time to research parameters during training the LSSVM model is significantly reduced. The proposed method can achieve accuracy rate of defect magnet of about 99.09%. At the same time, the overall accuracy rate is about 95.56%. The aim of classification flipped for defect magnet has been realized.