In the real-world problems, there is an imbalance in the paired-samples. The number of the paired-samples in similarity set is much smaller than the number of the paired-samples in dissimilarity set. To solve this problem, two approaches, dissimilar K nearest neighbor and similar K nearest neighbor (DKNN-SKNN) and dissimilar K nearest neighbor and similar K farthest neighbor (DKNN-SKFN), are proposed to construct paired-samples. Thus, the number of paired-samples in similarity learning is effectively decreased, the training process of SVM is accelerated, and the imbalanced data problem is solved to some degree. In the experiments, the proposed approaches are compared with some standard resampling methods. The results show that the proposed approaches have better performance.
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