HetEOTL: An Algorithm for Heterogeneous Online Transfer Learning

Abstract

Transfer learning is an important topic in machine learning and has been broadly studied for many years. However, most existing transfer learning methods assume the training sets are prepared in advance, which is often not the case in practice. Fortunately, online transfer learning (OTL), which addresses the transfer learning tasks in an online fashion, has been proposed to solve the problem. This paper mainly focuses on the heterogeneous OTL, which is in general very challenging because the feature space of target domain is different from that of the source domain. In order to enhance the learning performance, we designed the algorithm called Heterogeneous Ensembled Online Transfer Learning (HetEOTL) using ensemble learning strategy. Finally, we evaluate our algorithm on some benchmark datasets, and the experimental results show that HetEOTL has better performance than some other existing online learning and transfer learning algorithms, which proves the effectiveness of HetEOTL.