SETRED: Self-training with Editing

Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the self-training process may erroneously label some unlabeled examples, sometimes the learned hypothesis does not perform well. In this paper, a new algorithm named Setred is proposed, which utilizes a specific data editing method to identify and remove the mislabeled examples from the self-labeled data. In detail, in each iteration of the self-training process, the local cut edge weight statistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge

• Type: Conference
• Year: 2005
• Where: PAKDD
• Authors: Ming Li, Zhi-Hua Zhou
• Keywords: Data Mining, PAKDD 2005, Self-training Process, Semi-supervised Learning Algorithm, Unlabeled Examples