Abstract
Machine learning is useful for building robust learning models, and it is based on a set of features that identify a state of an object. Unfortunately, some data sets may contain a large number of features making, in some cases, the learning process time consuming and the generalization capability of machine learning poor. To make a data set easy to learn and understand, it is typically recommended to remove the most irrelevant features from the set. However, choosing what data should be kept or eliminated may be performed by complex selection algorithms, and optimal feature selection may require an exhaustive search of all possible subsets of features which is computationally expensive. This paper proposes a simple method to perform feature selection using artificial neural networks. It is shown experimentally that genetic algorithms in combination with artificial neural networks can easily be used to extract those features that are required to produce a desired result. Experimental results show that very few hidden neurons are required for feature selection as artificial neural networks are only used to assess the quality of an individual, which is a chosen subset of features.
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References
Alizadeh, A.A.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Bonev, B., Escolano, F., Carzola, M.A.: A Novel Information Theory Method for Filter Feature Selection. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 431–440. Springer, Heidelberg (2007)
Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3(2), 185–205 (2005)
Garber, M.E., Troyanskaya, O.G., et al.: Diversity of gene expression in adenocarcinoma of the lung. PNAS USA 98(24), 13784–13789 (2001)
Jones, M.T.: AI Application Programming, Charles River Media, 2nd edn., pp. 229–261 (2005)
Masters, T.: Practical Neural Network Recipes in C++, pp. 135–164. Academic Press, Inc., London (1993)
Peng, H., Ding, C., Long, F.: Minimum redundancy maximum relevance feature selection. IEEE Intelligent Systems 20(6), 70–71 (2005)
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 27(8), 1226–1238 (2005)
Reed, R.D., Marks II, R.J.: Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, pp. 185–195. The MIT Press, Cambridge (1999)
Russel, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, pp. 109–134. Prentice-Hall of India, Englewood Cliffs (2006)
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Ledesma, S., Cerda, G., Aviña, G., Hernández, D., Torres, M. (2008). Feature Selection Using Artificial Neural Networks. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_34
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DOI: https://doi.org/10.1007/978-3-540-88636-5_34
Publisher Name: Springer, Berlin, Heidelberg
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