A Semi-Supervised Rough Set Model for Classification Based on Active Learning and Co-Training
GAO Can, MIAO Duo-Qian, ZHANG Zhi-Fei, LIU Cai-Hui
Department of Computer Science and Technology,College of Electronics and Information Engineering,Tongji University,Shanghai 201804 Key Laboratory of Embedded System and Service Computing,Ministry of Education,Tongji University,Shanghai 201804
Abstract Rough set theory, as an effective supervised learning model, usually relies on the availability of an amount of labeled data to train the classifier. Howerer, in many practical problems, large amount of unlabeled data are readily available, and labeled ones are fairly expensive to obtain because of high cost. In this paper, a semi-supervised rough set model is proposed to deal with the partially labeled data. The proposed model firstly employs two diverse semi-supervised reducts to train its base classifiers on labeled data. The unlabeled ramified samples for two base classifiers are selected to be labeled based on the principle of active learning, and then the updated classifiers learn from each other by labeling confident unlabeled samples to its concomitant. The experimental results on selected UCI datasets show that the proposed model greatly improves the classification performance of partially labeled data, and even the best performance of dataset is obtained.
GAO Can,MIAO Duo-Qian,ZHANG Zhi-Fei等. A Semi-Supervised Rough Set Model for Classification Based on Active Learning and Co-Training[J]. , 2012, 25(5): 745-754.
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2012, Vol. 25 
