A Selection Metric for semi-supervised learning based on neighborhood construction

Abstract

The present paper focuses on semi-supervised classification problems. Semi-supervised learning is a learning task through both labeled and unlabeled samples. One of the main issues in semi-supervised learning is to use a proper selection metric for sampling from the unlabeled data in order to extract informative unlabeled data points. This is indeed vital for the semi-supervised self-training algorithms. Most self-training algorithms employ the probability estimations of the underlying base learners to select high-confidence predictions, which are not always useful for improving the decision boundary. In this study, a novel self-training algorithm is proposed based on a new selection metric using a neighborhood construction algorithm. We select unlabeled data points that are close to the decision boundary. Although these points are not high-confidence based on the probability estimation of the underlying base learner, they are more effective for finding an optimal decision boundary. To assign the correct labels to these data points, we propose an agreement between the classifier predictions and the neighborhood construction algorithm. The proposed approach uses a neighborhood construction algorithm employing peak data points and an Apollonius circle for sampling from unlabeled data. The algorithm then finds the agreement between the classifier predictions and the neighborhood construction algorithm to assign labels to unlabeled data at each iteration of the training process. The experimental results demonstrate that the proposed algorithm can effectively improve the performance of the constructed classification model.