A MultiBoosting Based Transfer Learning Algorithm

Abstract

Ensemble learning is sophisticated machine learning use to solve many problems in practical applications. MultiBoosting, a cutting-edge learning approach in ensemble learning, is combined with AdaBoost and wagging. It retains AdaBoost’s bias reduction while adding wagging’s variance reduction to that already obtained by AdaBoost, thus reducing the total number of errors in classification. Data characteristics do not always follow traditional machine learning rules, however, so transfer learning acts to solve this problem. We propose a TrMultiBoosting algorithm, composed of MultiBoosting and state-of-the-art transfer learning algorithm TrAdaBoost for transfer learning. We use naive bayes as the basic learning algorithm. TrMultiBoosting has proven to present a decision committee with higher prediction accuracy on UCI data sets than either TrAdaBoost or MultiBoosting.