An evolutionary multi-objective approach to learn from positive and unlabeled data

Abstract

Positive and unlabeled (PU) learning has attracted increasing interests in recent years. Despite that a number of PU learning algorithms have been proposed, most of them are subject to some assumptions about unlabeled sample distribution and objective functions, which makes them difficult to be adopted for real applications. To this end, in this paper, an evolutionary multi-objective approach, namely MOEA-PUL, is suggested for PU learning, whose aim is to build a PU classifier without any prior assumption for data distribution and objective functions. To be specific, the PU learning is formulated as a bi-objective optimization problem, where the true positive rate (TPR) and a new metric, termed unlabeled accuracy rate (UAR) are used as the two objectives. A multi-objective evolutionary algorithm is proposed to solve this bi-objective optimization problem, under the framework of NSGA-II, where a PU similarity based initialization strategy and an elite label based learning strategy are developed. Empirical studies on 12 datasets demonstrate the superiority of MOEA-PUL over the existing PU learning algorithms.