A robust semi-supervised classification method for transfer learning
Akinori Fujino, Naonori Ueda, Masaaki Nagata · 2010
The transfer learning problem of designing good classifiers with a high generalization ability by using labeled samples whose distribution is different from that of test samples is an important and challenging research issue in the fields of machine learning and data mining. This paper focuses on designing a semi-supervised classifier trained by using unlabeled samples drawn by the same distribution as test samples, and presents a semi-supervised classification method to deal with the transfer learning problem, based on a hybrid discriminative and generative model. Although JESS-CM is one of the most successful semi-supervised classifier design frameworks and has achieved the best published results in NLP tasks, it has an overfitting problem in transfer learning settings that we consider in this paper. We expect the overfitting problem to be mitigated with the proposed method, which utilizes both labeled and unlabeled samples for the discriminative training of classifiers. We also present a refined objective that formalizes the training algorithm and classifier form. Our experimental results for text classification using three typical benchmark test collections confirmed that the proposed method outperformed the JESS-CM framework with most transfer learning settings.