• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
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  • 한국과학기술단체총연합회
  • 한국학술지인용색인
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Title Semisupervised Learning Using the AdaBoost Algorithm with SVM-KNN
Authors 이상민(Lee, Sang-Min) ; 연준상(Yeon, Jun-Sang) ; 김지수(Kim, Ji-Soo) ; 김성수(Kim, Sung-Soo)
DOI https://doi.org/10.5370/KIEE.2012.61.9.1336
Page pp.1336-1339
ISSN 1975-8359
Keywords Semisupervised learning ; SVM ; KNN ; AdaBoost
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.