A framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification

Self-labeled techniques are semi-supervised classification models that overcome the shortage of labeled samples via an iterative process. Most relevant proposals are inspired by boosting schemes to iteratively enlarge labeled data, but these methods are constrained by the number and distribution of the initial labeled data. Up to the present, the only exceptions which can solve the above problem are SEG-SSC, k-means-SSC and LC-SSC. However, SEG-SSC relies on too many parameters. Besides, it is hard to improve the distribution of the initial labeled data when the initial labeled set can not roughly represent the distribution of the original data. k-means-SSC and LC-SSC fail to significantly improve the number of the initial labeled data by a limited number of representative points. To address the above issues, this paper proposes a framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification (LCSEG-SSC). First, a new method for finding local cores on labeled and unlabeled data is proposed to improve the distribution of the initial labeled data. Second, STOPF or active labeling is used to predict found local cores. Third, a new example generation technique is proposed to create synthetic labeled samples, intending to improve the number of the initial labeled data. After that, any self-labeled with boosting schemes can be executed on the improved labeled data effectively. Intensive experiments prove that LCSEG-SSC outperforms state-of-the-art methods, especially in a relatively low ratio of labeled data.