AdaBoost-based transfer learning method for positive and unlabelled learning problem

Abstract

Positive and unlabelled learning (PU learning) is a problem that the training of a classifier only utilizes labelled positive examples and unlabelled examples. Recently, PU learning has been widely studied and used in a number of areas. In this paper, we present an AdaBoost-based transfer learning method to solve PU Learning problem, which is briefly called AdaTLPU. In the proposed model, by sharing SVM parameters and regularization terms, the source task knowledge is transferred to the target task. At the same time, the similarity of the ambiguous examples towards the positive and negative classes is taken into account to refine the decision boundary of the classifier. Meanwhile, we adopt the AdaBoost method to ensemble the obtained weak classifiers to form a strong classifier for prediction. In addition, we put forward an iterative optimization method to obtain the classifier and present the proof of training error bound for the proposed method. Finally, we organize experiments to explore the performance of AdaTLPU and the results indicate that AdaTLPU can achieve the better performance compared with previous PU learning methods.