Integrated Transfer Learning Algorithm Using Multi-source TrAdaBoost for Unbalanced Samples Classification

Abstract

To solve the binary classification transfer learning problem with similar data distributions and class imbalance between positive and negative samples in the target and source domains, we present an integrated transfer learning algorithm for multi-source unbalanced samples classification. We try to avoid the negative transfer problem by utilizing multiple source domains, and propose the new sample weights initialization and weights updating strategies to solve the class imbalance problem. Moreover, we propose a new elimination mechanism to eliminate the redundant samples in the multiple source domains, and then the time and memory costs of the classifier could be significantly reduced. Experimental results on standard UCI datasets show that the proposed algorithm outperforms the state-of-the-arts transfer learning algorithms in terms of F1-measure and AUC evaluations metrics.