Abstract
Semi-supervised feature selection has become more important as the number of features has increased in partially labeled data sets. In this paper we present a feature weighting-based model to address this problem. Our proposal is based on a semi-supervised clustering paradigm that can rank features according to their relevance from high-dimensional data. We propose an adaptation of the constrained K-Means algorithm to semi-supervised feature selection by an embedded approach. Experiments are provided on several known data sets for validating our proposal. The results are promising and competitive with several representative methods.
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References
Guan, Y., Dy, J., Jordan, M.: A unified probabilistic model for global and local unsupervised feature selection. In: Proceedings of the Twenty Eight International Conference on Machine Learning (2011)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Dy, J., Brodley, C.E.: Feature selection for unsupervised learning. J. Mach. Learn. Res. 5, 845–889 (2004)
Zhao, Z., Liu, H.: Semi-supervised feature selection via spectral analysis. In: Proceedings of SIAM International Conference on Data Mining, pp. 641–646 (2007)
Zhang, D., Zhou, Z., Chen, S.: Semi-supervised dimensionality reduction. In: Proceedings of SIAM International Conference on Data Mining (2007)
Zhang, D., Chen, S., Zhou, Z.: Constraint score: a new filter method for feature selection with pairwise constraints. Pattern Recogn. 41(5), 1440–1451 (2008)
Kalakech, M., Biela, P., Macaire, L., Hamad, D.: Constraint scores for semi-supervised feature selection: a comparative study. Pattern Recogn. Lett. 32(5), 656–665 (2011)
Benabdeslem, K., Hindawi, M.: Constrained laplacian score for semi-supervised feature selection. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part I. LNCS, vol. 6911, pp. 204–218. Springer, Heidelberg (2011)
Hindawi, M., Allab, K., Benabdeslem, K.: Constraint selection based semi-supervised feature selection. In: Proceedings of International Conference on Data Mining, pp. 1080–1085 (2011)
Benabdeslem, K., Hindawi, M.: Efficient semi-supervised feature selection: constraint, relevance and redundancy. IEEE Trans. Knowl. Data Eng. 26(5), 1131–1143 (2014)
Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Clustering with instance level constraints. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 1103–1110 (2001)
Bilenko, M., Basu, S., Mooney, R.: Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the Twenty First International Conference on Machine Learning, pp. 11–18 (2004)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Benabdeslem, K., Hindawi, M., Makkhongkaew, R. (2015). Weighting Based Approach for Semi-supervised Feature Selection. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_36
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DOI: https://doi.org/10.1007/978-3-319-26561-2_36
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