Abstract
Relevance is a central concept in many feature selection algorithms. Given a relevance measure, a feature selection algorithm searches for a subset of features that maximise the relevance between the subset and target concepts. This paper first shows how relevance measures that rely on the posterior estimation such as information theory measures may fail to quantify the actual utility of subsets of features in certain situations. The paper then proposes a solution based on Genetic Programming which can improve the usability of these measures. The paper is focused on classification problems with numeric features.
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References
Carnap, R.: Logical foundations of probability. University of Chicago Press (1967)
Davis, L.: Adapting operator probabilities in genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 61–69. Morgan Kaufmann Publishers Inc., George Mason University, United States (1989)
Gärdenfors, P.: On the logic of relevance. Synthese 37(3), 351–367 (1978)
Keynes, J.: A treatise on probability. Macmillan & Co., Ltd. (1921)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Last, M., K, A., Maimon, O.: Information-theoretic algorithm for feature selection. Pattern Recognition Letters 22, 799–811 (2001)
Neshatian, K., Zhang, M.: Unsupervised Elimination of Redundant Features Using Genetic Programming. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 432–442. Springer, Heidelberg (2009)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1226–1238 (2005)
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd. (March 2008)
Quinlan, J.R.: Improved use of continuous attributes in C4. 5. Journal of Artificial Intelligence Research 4, 77–90 (1996)
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© 2012 Springer-Verlag Berlin Heidelberg
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Neshatian, K., Zhang, M. (2012). Improving Relevance Measures Using Genetic Programming. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds) Genetic Programming. EuroGP 2012. Lecture Notes in Computer Science, vol 7244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29139-5_9
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DOI: https://doi.org/10.1007/978-3-642-29139-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29138-8
Online ISBN: 978-3-642-29139-5
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