Imbalanced defect classification for mobile phone screen glass using multifractal features and a new sampling method
Defect classification has drawn significant attention in the mobile phone screen glass (MPSG) manufacturing field because it helps to determine problems in the manufacturing process. Two problems exist in MPSG defect classification: (1) the high dimensionality of the defect feature; (2) imbalanced defect example classification. The first problem tends to yield low accuracy for classifying overall defect examples, and the second problem has a low accuracy for minority ones. To address these two problems, an imbalanced MPSG defect classification scheme is presented. First, based on the multifractal spectrum, defect features are extracted to reduce the feature dimensionality. Defect features are distinguishably characterized by two multifractal metrics to promote the performance of classifying defects. Second, considering example contributions to determine the classification boundary, a new sampling method is proposed to address the imbalanced defect example classification. This method improves the classification accuracy of the minority class through implementation of different sampling strategies to SVs (support vectors) and NSVs (non support vectors) in the majority and minority classes. Experiments are conducted on real MPSG defect examples, and the experimental results show that the imbalanced MPSG defect classification scheme achieves a 96.61% overall accuracy and a 93.27% geometric mean of the classification accuracies of four type defects; these results are superior to the results achieved by other methods used in the experiment.
Multimedia Tools and Applications