Automatic Adjustment of Confidence Values in Self-training Semi-supervised Method

This paper consists of a study in the field of semisupervised learning and implements changes on the self-training algorithm in order to propose a variation in the rate of inclusion of new observations in the labeled dataset. In order to achieve this goal, three methods (FlexCon-G, FlexCon e FlexCon-C) are proposed, which differ in the way that they perform the calculation of a new value for the minimum confidence rate to include new patterns. In order to evaluate the proposed methods, we performed experimentations with 20 datasets with diversified characteristics. Each of them was setup with a different percentage of initially labeled patterns. Each dataset was trained using the Naive Bayes, decision tree and ripper classifiers. Moreover, Friedmann statistical test was applied to provide a statistically significant analysis. The obtained results indicate that the three proposed methods perform better than a self-training method in most cases, pointing to the FlexCon-C method as the most efficient of them.