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Using clustering analysis to improve semi-supervised classification
- Source :
-
Neurocomputing . Feb2013, Vol. 101, p290-298. 9p. - Publication Year :
- 2013
-
Abstract
- Abstract: 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 101
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 83323228
- Full Text :
- https://doi.org/10.1016/j.neucom.2012.08.020