The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Semisupervised Learning Using the AdaBoost Algorithm with SVM-KNN

SVM-KNN-AdaBoost를 적용한 새로운 중간교사학습 방법

  • Received : 2012.07.16
  • Accepted : 2012.08.24
  • Published : 2012.09.01
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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.

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References

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