Semi-Supervised Learning Based on One-Class Classification
MIAO Zhi-Min1, ZHAO Lu-Wen1, HU Gu-Yu2,WANG Qiong2
1.Institute of Communication Engineering, PLA University of Science and Technology, Nanjing 210007 2.Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007
Abstract A semi-supervised learning algorithm is proposed based on one-class classification. Firstly, one-class classifications are built respectively for each class of data on labeled dataset. Then, some unlabeled data are tested by these one-class classifications. The classification results are used to adjust and optimize two classification surfaces. All labeled data and some recognized unlabeled data are used to train a base classifier. According to the classifying results of the base classifiers, the label of the test sample is determined. Experimental results on UCI datasets illustrate that the average detection precision of the proposed algorithm is 4.5% higher than that of the tri-training algorithm and 8.9% higher than that of the classifier trained by pure labeled data.
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2009, Vol. 22 
