A self-training semi-supervised classification algorithm based on density peaks of data and differential evolution

Self-training semi-supervised classification methodology is highly effective in alleviating the shortage of labeled data in classification tasks via an iterative self-training process. In this paper, we propose a self-training semi-supervised classification algorithm based on density peaks of data and differential evolution. The proposed algorithm consists of two main parts. First part is to use the underlying structure of data space, which is discovered based on density peaks of data, to help train a better classifier. Second part is to use the differential evolution to optimize the positioning of newly labeled data during the self-training process, where newly labeled data denotes the unlabeled data labeled by classifier during the self-training process and optimizing the positioning means optimally adjusting the attributes values of date. Experimental results on 12 benchmark datasets clearly demonstrate that the proposed algorithm is more effective than some previous works in improving the performance of base classifier of support vector machine or k-nearest neighbor.