Abstract
This paper presents a Semi-Supervised Feature Selection Method based on a univariate relevance measure applied to a multiobjective approach of the problem. Along the process of decision of the optimal solution within Pareto-optimal set, atempting to maximize the relevance indexes of each feature, it is possible to determine a minimum set of relevant features and, at the same time, to determine the optimal model of the neural network.
Chapter PDF
References
Niyogi, P., Belkin, M.: Semi-supervised learning on riemannian manifolds. Machine Learning 56, 209–239 (2004)
Coelho, F., de Braga, A.P., Natowicz, R., Rouzier, R.: Semi-supervised model applied to the prediction of the response to preoperative chemotherapy for breast cancer. In: Soft Computing - A Fusion of Foundations, Methodologies and Applications (July 2010)
Le Cun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: Advances in Neural Information Processing Systems, pp. 598–605. Morgan Kaufmann, San Francisco (1990)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making Theory and Methodology. Elsevier Science, New York (1983)
Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)
Dy, J.G., Brodley, C.E.: Feature selection for unsupervised learning. J. Mach. Learn. Res. 5, 845–889 (2004)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals Eugen. 7, 179–188 (1936)
Kasabov, N., Pang, S.: Transductive support vector machines and applications in bioinformatics for promoter recognition. In: Proc. of International Conference on Neural Network & Signal Processing, Nangjing. IEEE Press, Los Alamitos (2004)
Kira, K., Rendell, L.A.: The feature selection problem: Traditional methods and a new algorithm. In: AAAI, Cambridge, MA, USA, pp. 129–134. AAAI Press and MIT Press (1992)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: ML 1992: Proc. of the Ninth International Workshop on Machine Learning, pp. 249–256. Morgan Kaufmann Publishers Inc., San Francisco (1992)
Kruskal, J., Wish, M.: Multidimensional Scaling. Sage Publications, Thousand Oaks (1978)
Liang, F., Mukherjee, S., West, M.: The use of unlabeled data in predictive modeling. Statistical Science 22, 189 (2007)
Lawler, E.L., Wood, D.E.: Branch-and-bound methods: A survey. Operations Research 14(4), 699–719 (1966)
Malerba, D., Ceci, M., Appice, A.: A relational approach to probabilistic classification in a transductive setting. Eng. Appl. Artif. Intell. 22(1), 109–116 (2009)
Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)
Parma, G.G., Menezes, B.R., Braga, A.P., Costa, M.A.: Sliding mode neural network control of an induction motor drive. Int. Jour. of Adap. Cont. and Sig. Proc. 17(6), 501–508 (2003)
Press, W.H., Teukolsky, S.A., Vetterling, W.T.: Numerical recipes in C (2nd ed.): the art of scientific computing. Cambridge University Press, New York (1992)
Takahashi, R.H.C., Teixeira, R.A., Braga, A.P., Saldanha, R.R.: Improving generalization of MLPs with multi-objective optimization. Neurocomputing 35(1-4), 189–194 (2000)
Wu, J., Yu, L., Meng, W., Shu, L.: Kernel-based transductive learning with nearest neighbors. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, Q.-M. (eds.) APWeb/WAIM 2009. LNCS(LNAI), vol. 5446, pp. 345–356. Springer, Heidelberg (2009)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(5500), 2319–2323 (2000)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Wang, J., Shen, X., Pan, W.: On efficient large margin semisupervised learning: Method and theory. J. Mach. Learn. Res. 10, 719–742 (2009)
Zhang, D., Zhou, Z.-h., Chen, S.: Semi-Supervised Dimensionality Reduction. In: SIAM Conference on Data Mining (SDM), pp. 629–634 (2007)
Bland, R.G., Goldfarb, D., Todd, M.J.: The Ellipsoid Method: A Survey. Operations Research 29(6), 1039–1091 (1980)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Coelho, F., Braga, A.P., Verleysen, M. (2010). Multi-Objective Semi-Supervised Feature Selection and Model Selection Based on Pearson’s Correlation Coefficient. In: Bloch, I., Cesar, R.M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2010. Lecture Notes in Computer Science, vol 6419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16687-7_67
Download citation
DOI: https://doi.org/10.1007/978-3-642-16687-7_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16686-0
Online ISBN: 978-3-642-16687-7
eBook Packages: Computer ScienceComputer Science (R0)