Theoretical and Methodological Advances in Semi-supervised Learning and the Class-Imbalance Problem

Abstract

his paper focuses on the theoretical and practical generalization of two known and challenging situations from the field of machine learning to classification problems in which the assumption of having a single binary class is not fulfilled.semi-supervised learning is a technique that uses large amounts of unlabeled data to improve the performance of supervised learning when the labeled data set is very limited. Specifically, this work contributes with powerful and computationally efficient methodologies to learn, in a semi-supervised way, classifiers for multiple class variables. Also, the fundamental limits of semi-supervised learning in multi-class problems are investigated in a theoretical way. The problem of class unbalance appears when the target variables present a probability distribution unbalanced enough to distort the solutions proposed by the traditional supervised learning algorithms. In this project, a theoretical framework is proposed to separate the deviation produced by class unbalance from other factors that affect the accuracy of classifiers. This framework is mainly used to make a recommendation of classifier assessment metrics in this situation. Finally, a measure of the degree of class unbalance in a data set correlated with the loss of accuracy caused is also proposed.