A transfer-learning approach to exploit noisy information for classification and its application on sentiment detection

Abstract

This research proposes a novel transfer learning algorithm, Noise-Label Transfer Learning (NLTL), aiming at exploiting noisy (in terms of labels and features) training data to improve the learning quality. We exploit the information from both accurate and noisy data by transferring the features into common domain and adjust the weights of instances for learning. We experiment on three University of California Irvine (UCI) datasets and one real-world dataset (Plurk) to evaluate the effectiveness of the model.