Ensemble Inductive Transfer Learning (Journal of Fiber Bioengineering and Informatics, 2015)
Inductive transfer learning is a major research area in transfer learning which aims at achieving a\rhigh performance in the target domain by inducing the useful knowledge from the source domain. By\rcombining decisions from individual classifiers, ensemble learning can usually reduce variance and achieve\rhigher accuracy than a single classifier. In this paper, we propose a novel Ensemble Inductive Transfer\rLearning (EITL) method. EITL builds a set of classifiers by recording the iterative process of knowledge\rtransfer. In each iteration, it uses the classifier of the source domain, the base classifier of the target\rdomain built on the initial labeled data, and the most recent classifier built on the updated labeled\rdata, to classify unlabeled instances, and add some self-labeled instances to the labeled data, and then\rtrains a new classifier. At the end, all the classifiers built in this process are used for classification. We\rconduct experiments on synthetic data sets and six UCI data sets, which show that EITL is an effective\ralgorithm in terms of classification accuracy.