Using decomposition-based multi-objective evolutionary algorithm as synthetic example optimization for self-labeling

摘要

Existing a lot of unlabeled data and few labeled data is one of the most common problems in real datasets. Semi-supervised classification methods can well handle such a problem and have a desirable performance. Among them, one of the most successful methods in dealing with shortage of labeled data is self-labeled technique. One of the difficulties of this technique is wrong data labeling in iterative process of self-labeling. The main reasons are 1) existing outlier and noisy data, 2) inappropriate distribution of labeled data in problem space, and 3) shortage of labeled data in order to make diversity in learning hypotheses. In this paper, a method is developed so as to generate synthetic labeled using decomposition-based multi-objective evolutionary algorithm as synthetic example optimization for self-labeling called DMSS. In DMSS, the synthetic labeled datasets with high diversity and high classification accuracy are generated and then added to the labeled datasets for better training of the algorithm. In already conducted researches, the diversity of the generated data and their distribution in problem space have not been well investigated. The proposed method is a data preparation method which can be employed in all self-labeled techniques. To do so, the proposed method is applied over four self-labeled algorithms having different features and their performances are then evaluated using 25 pattern datasets. The obtained results show high performance of the DMSS with regard to the classification accuracy compared to the existing methods in the literature. Also, the outcomes of conducted non-parametric statistical tests show that the proposed method significantly outperforms the other existing methods in the literature, as well.