Ensemble Inductive Transfer Learning

Abstract

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