Semi-supervised learning based on nearest neighbor rule and cut edges

Authors:
Yu Wang;Xiaoyan Xu;Haifeng Zhao;Zhongsheng Hua
Affiliations:
Department of Information Management and Information Systems, School of Economics and Business Administration, Chongqing University, Chongqing 400030, PR China and School of Management, University ...;School of Management, University of Science and Technology of China, Hefei, Anhui 230026, PR China;School of Economic and Management, Tongji University, Shanghai 200092, PR China;School of Management, University of Science and Technology of China, Hefei, Anhui 230026, PR China
Venue:
Knowledge-Based Systems
Year:
2010

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Abstract

In this paper, we propose a novel semi-supervised learning approach based on nearest neighbor rule and cut edges. In the first step of our approach, a relative neighborhood graph based on all training samples is constructed for each unlabeled sample, and the unlabeled samples whose edges are all connected to training samples from the same class are labeled. These newly labeled samples are then added into the training samples. In the second step, standard self-training algorithm using nearest neighbor rule is applied for classification until a predetermined stopping criterion is met. In the third step, a statistical test is applied for label modification, and in the last step, the remaining unlabeled samples are classified using standard nearest neighbor rule. The main advantages of the proposed method are: (1) it reduces the error reinforcement by using relative neighborhood graph for classification in the initial stages of semi-supervised learning; (2) it introduces a label modification mechanism for better classification performance. Experimental results show the effectiveness of the proposed approach.