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http://dx.doi.org/10.5370/KIEE.2012.61.9.1336

Semisupervised Learning Using the AdaBoost Algorithm with SVM-KNN  

Lee, Sang-Min (충북대학교 전기공학과)
Yeon, Jun-Sang (충북대학교 전기공학과)
Kim, Ji-Soo (충북대학교 지구환경과학과)
Kim, Sung-Soo (충북대학교 전기공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.61, no.9, 2012 , pp. 1336-1339 More about this Journal
Abstract
In this paper, we focus on solving the classification problem by using semisupervised learning strategy. Traditional classifiers are constructed based on labeled data in supervised learning. Labeled data, however, are often difficult, expensive or time consuming to obtain, as they require the efforts of experienced human annotators. Unlabeled data are significantly easier to obtain without human efforts. Thus, we use AdaBoost algorithm with SVM-KNN classifier to apply semisupervised learning problem and improve the classifier performance. Experimental results on both artificial and UCI data sets show that the proposed methodology can reduce the error rate.
Keywords
Semisupervised learning; SVM; KNN; AdaBoost;
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