ADE-Tri-training:Tri-training with Adaptive Data Editing
Guo Mao · Chinese Journal of Computers · 2007
Tri-training, a Co-training style semi-supervised learning algorithm, can effectively exploit unlabeled examples to improve generalization ability. However, Tri-training may suffer more from the common problem in semi-supervised learning, i.e. the performance is usually not stable due to the unlabeled examples may often be wrongly labeled and accumulated during the iterative learning process. In this paper a new Tri-training style algorithm named ADE-Tri-training (Tri-training with Adaptive Data Editing) is proposed. ADE-Tri-training not only employs a specific Data Editing technique to identify and discard possible mislabeled examples along with iterations of three classifiers mutually labeling, but also takes an adaptive strategy to trigger or inhibit the editing operation according to different situation. The adaptive strategy is combinations of five precondition theorems all that will ensure reducing classification error as well as increasing the scale of new training set iteratively under the PAC theory. This paper also provides the proof of all these precondition theorems. Experiments on UCI datasets show that ADE-Tri-training could more effectively and stably utilize the unlabeled examples to improve classification generalization than Tri-training and DE-Tri-training (Tri-training with Data Editing but without adaptive strategy).