Participatory Learning Based Semi-Supervised Classification

Abstract

Mislabeling unlabeled data during the learning process is an inevitable problem for the co-training style semi-supervised learning. In this paper, the participatory learning cognition paradigm is instantiated through employing the data editing as acceptance unit and designing an arousal strategy of data editing as critic unit. Then, this participatory learning is equipped into each individual classifier of Tri-training, a co-training style semi-supervised approach, and forms a new algorithm named PL-Tri-training (Participatory Learning based Tri-training). In the co-training process of PL-tri training,the acceptance unit utilizes data editing to identify and remove the mislabeled data, as well as the critic unit exploits arousal strategy to inhibit the invalid activation of data editing. The experiments on UCI datasets show that PL-Tri-training can more effectively and stably exploit the unlabeled data to improve the classification performance than Tri-training and DE-Tri training, which equips the Tri-training with only the data editing acceptance unit of participatory learning.