Self-trained eXtreme Gradient Boosting Trees

Abstract

Semi-Supervised Learning (SSL) is an ever-growing research area offering a powerful set of methods, either single or multi-view, for exploiting both labeled and unlabeled instances in the most effective manner. Self-training is a representative SSL algorithm which has been efficiently implemented for solving several classification problems in a wide range of scientific fields. Moreover, self-training has served as the base for the development of several self-labeled methods. In addition, gradient boosting is an advanced machine learning technique, a boosting algorithm for both classification and regression problems, which produces a predictive model in the form of decision trees. In this context, the principal objective of this paper is to put forward an improved self-training algorithm for classification tasks utilizing the efficacy of eXtreme Gradient Boosting (XGBoost) trees in a self-labeled scheme in order to build a highly accurate and robust classification model. A number of experiments on benchmark datasets were executed demonstrating the superiority of the proposed method over representative semi-supervised methods, as statistically verified by the Friedman non-parametric test.