Journal
NEUROCOMPUTING
Volume 101, Issue -, Pages 290-298Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2012.08.020
Keywords
Self-training; Semi-supervised classification; Semi-supervised clustering; Fuzzy c-means; Support vector machine
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Funding
- National Natural Science Foundation of China [61072143, 61105014, 61170093]
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Semi-supervised classification has become an active topic recently and a number of algorithms, such as Self-training, have been proposed to improve the performance of supervised classification using unlabeled data. In this paper, we propose a semi-supervised learning framework which combines clustering and classification. Our motivation is that clustering analysis is a powerful knowledge-discovery tool and it may reveal the underlying data space structure from unlabeled data. In our framework, semi-supervised clustering is integrated into Self-training classification to help train a better classifier. In particular, the semi-supervised fuzzy c-means algorithm and support vector machines are used for clustering and classification, respectively. Experimental results on artificial and real datasets demonstrate the advantages of the proposed framework. (C) 2012 Elsevier B.V. All rights reserved.
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