Volume 5 Number 5 (May 2010)
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JCP 2010 Vol.5(5): 671-678 ISSN: 1796-203X
doi: 10.4304/jcp.5.5.671-678

Semi-supervised Learning for SVM-KNN

Kunlun Li1, Xuerong Luo1, 2, and Ming Jin1, 2
1 College of Electronics and information Engineering, Hebei University, Baoding, 071002,china
2 School of Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876,china

Abstract—Compared with labeled data, unlabeled data are significantly easier to obtain. Currently, classification of unlabeled data is an open issue. In this paper a novel SVMKNN classification methodology based on Semi-supervised learning is proposed, we consider the problem of using a large number of unlabeled data to boost performance of the classifier when only a small set of labeled examples is available. We use the few labeled data to train a weaker SVM classifier and make use of the boundary vectors to improve the weaker SVM iteratively by introducing KNN. Using KNN classifier doesn’t enlarge the number of training examples only, but also improves the quality of the new training examples which are transformed from the boundary vectors. Experiments on UCI data sets show that the proposed methodology can evidently improve the accuracy of the final SVM classifier by tuning the parameters and can reduce the cost of labeling unlabeled examples.

Index Terms—semi-supervised learning, support vector machine, K-nearest neighbor, boundary vectors


Cite: Kunlun Li, Xuerong Luo, and Ming Jin, " Semi-supervised Learning for SVM-KNN," Journal of Computers vol. 5, no. 5, pp. 671-678, 2010.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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