کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
407602 | 678158 | 2013 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Using clustering analysis to improve semi-supervised classification Using clustering analysis to improve semi-supervised classification](/preview/png/407602.png)
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.
Journal: Neurocomputing - Volume 101, 4 February 2013, Pages 290–298