Abstract
This paper presents a comparison of the methods of selecting a small amount useful unlabeled data to improve the classification accuracy of semi-supervised learning (SSL) algorithms. In particular, three selection approaches, namely, the simply adjusted approach based on an uncertainty level, the normalized-and-adjusted approach, and the entropy based adjusted approach, are considered and compared empirically. The experimental results, which are obtained from synthetic and real-life benchmark data using semi-supervised support vector machines (S3VMs), demonstrate that the entropy based approach works slightly better than the other ones in terms of the classification accuracy.
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Le, TB., Kim, SW. (2015). Comparison of Adjusted Methods for Selecting Useful Unlabeled Data for Semi-Supervised Learning Algorithms. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_51
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DOI: https://doi.org/10.1007/978-3-319-19066-2_51
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