IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
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A Hybrid Selection Method of Helpful Unlabeled Data Applicable for Semi-Supervised Learning Algorithm

  • Le, Thanh-Binh (Department of Computer Engineering, Myongji University) ;
  • Kim, Sang-Woon (Department of Computer Engineering, Myongji University)
  • Received : 2013.12.20
  • Accepted : 2013.05.12
  • Published : 2014.08.31
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Abstract

This paper presents an empirical study on selecting a small amount of useful unlabeled data to improve the classification accuracy of semi-supervised learning algorithms. In particular, a hybrid method of unifying the simply recycled selection method and the incrementally-reinforced selection method was considered and evaluated empirically. The experimental results, which were obtained from well-known benchmark data sets using semi-supervised support vector machines, demonstrated that the hybrid method works better than the traditional ones in terms of the classification accuracy.

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References

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