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http://dx.doi.org/10.5573/IEIESPC.2014.3.4.234

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)
Publication Information
IEIE Transactions on Smart Processing and Computing / v.3, no.4, 2014 , pp. 234-239 More about this Journal
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.
Keywords
Pattern recognition and machine learning; Semi-supervised learning (SSL); Simply recycled selection (SRS); Incrementally reinforced selection (IRS); Hybrid selection strategy (HYB);
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1 T.-B. Le and S.-W. Kim, "Simply recycled selection and incrementally reinforced selection methods applicable for semi-supervised learning algorithms," in Proc. of the 2014 Int'l Conf. on Electronics, Information and Communication (ICEIC 2014), pp. 15-18, Jan. 2014.
2 X. Zhu, A. B. Goldberg, Introduction to Semi-Supervised Learning, Morgan & Claypool, San Rafael, CA, 2009 doi:10.2200/S00196ED1V01Y200906AIM006   DOI
3 F. G. Cozman, I. Cohen, M. C. Cirelo, "Semisupervised learning of mixture models," in Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003.
4 D. Elworthy, "Does Baum-Welch re-estimation help taggers?" in Proceedings of the fourth conference on Applied natural language processing (ANLC'94), pp. 53-58, 1994. Doi:10.3115/974358.974371   DOI
5 C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," Journal ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, pp. 1-27, 2011. doi:10.1145/1961189.1961199   DOI
6 T.-B. Le and S.-W. Kim, "On incrementally using a small portion of strong unlabeled data for semisupervised learning algorithms," Pattern Recognition Letters, vol. 41, pp. 53-64, May 2014. doi:10.1016/j.patrec.2013.08.026   DOI
7 P. K. Mallapragada et al., "SemiBoost: Boosting for semi-supervised learning," IEEE Trans. Pattern Anal. and Machine Intell., vol. 312, no. 11, pp. 2000-2014, Nov. 2009. doi: 10.1109/TPAMI.2008.235.   DOI
8 A. Asuncion and D. J. Newman, "UCI Machine Learning Repository," Technical Report, University of California, School of Information and Computer Science, Irvine, CA, 2007.
9 O. Chapelle et al., "Semi-Supervised Learning," The MIT Press, MA, 2006.
10 M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, A. Zisserman, "The PASCAL Visual Object Classes (VOC) Challenge," Int J Comput Vis (2010) vol. 88. pp. 303-338, 2010. doi:10.1007/s11263-009-0275-4.   DOI   ScienceOn
11 A. Vedaldi et al., "Image Classification Practical 2011," The MIT Press, MA, 2006,
12 F. Wilcoxon, "Individual comparisons by ranking methods," Biometrics Bulletin, Vol. 1, No. 6, pp. 80-83, Dec 1945.   DOI   ScienceOn