Ensemble Transfer Learning Algorithm

Abstract

Transfer learning and ensemble learning are the new trends for solving the problem that training data and test data have different distributions. In this paper, we design an ensemble transfer learning framework to improve the classification accuracy when the training data are insufficient. First, a weightedresampling method for transfer learning is proposed, which is named TrResampling. In each iteration, the data with heavy weights in the source domain are resampled, and the TrAdaBoost algorithm is used to adjust the weights of the source data and target data. Second, three classic machine learning algorithms, namely, naive Bayes, decision tree, and SVM, are used as the base learners of TrResampling, where the base learner with the best performance is chosen for transfer learning. To illustrate the performance of TrResampling, the TrAdaBoost and decision tree are used for evaluation and comparison on 15 UCI data sets, TrAdaBoost, ARTL, and SVM are used for evaluation and comparison on five text data sets. According to the experimental results, our proposed TrResampling is superior to the state-of-the-art learning methods on UCI data sets and text data sets. In addition, TrResampling, bagging-based transfer learning algorithm, and MultiBoosting-based transfer learning algorithm (TrMultiBoosting) are assembled in the framework, and we compare the three ensemble transfer learning algorithms with TrAdaBoost to illustrate the framework's effective transfer ability.

Keywords

• Transfer learning
• bagging
• boosting
• ensemble learning