Abstract
A framework that combines feature selection with evolutionary artificial neural networks is presented. This paper copes with neural networks that are applied in classification tasks. In machine learning area, feature selection is one of the most common techniques for pre-processing the data. A set of filters have been taken into consideration to assess the proposal. The experimentation has been conducted on nine data sets from the UCI repository that report test error rates about fifteen percent or above with reference classifiers such as C4.5 or 1-NN. The new proposal significantly improves the baseline framework, both approaches based on evolutionary product unit neural networks. Also several classifiers have been tried in order to illustrate the performance of the different methods considered.
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References
Aggarwal, C.C.: An introduction to data classification. In: Data Classification: Algorithms and Applications, p. 1 (2014)
Bache, K., Lichman, M.: UCI machine learning repository online (2015)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Downing, K.L.: Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems. MIT Press, Cambridge (2015)
Durbin, R., Rumelhart, D.: Products units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Comput. 1(1), 133–142 (1989)
Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML ), pp. 359–366. Morgan Kaufmann, San Francisco (2000)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI ), vol. 14, pp. 1137–1145 (1995)
Kwak, N., Choi, C.-H.: Input feature selection for classification problems. IEEE Trans. Neural Netw. 13(1), 143–159 (2002)
Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining, vol. 454. Springer Science & Business Media, New York (2012)
Liu, H., Setiono, R.: A probabilistic approach to feature selection - a filter solution. In: Proceedings of the thirteenth International Conference on Machine Learning (ICML), pp. 319–327, Italy. Morgan Kaufmann (1996)
Prechelt, L.: Proben1: A set of neural network benchmark problems and benchmarking rules (1994)
Quinlan, J.R.: C4.5: Programming for machine learning. Morgan Kauffmann, San Francisco (1993)
Rojas, R.: Neural Networks: A Systematic Introduction. Springer Science & Business Media, New York (2013)
Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S.: Projection-based measure for efficient feature selection. J. Intell. Fuzzy Syst. 12(3–4), 175–183 (2002)
Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S.: Incremental wrapper-based gene selection from microarray expression data for cancer classification. Pattern Recogn. 39(12), 2383–2392 (2006)
Tallón-Ballesteros, A.J., Gutiérrez-Peña, P.A., Hervás-Martínez, C.: Distribution of the search of evolutionary product unit neural networks for classification. arXiv preprint (2012). arxiv:1205.3336
Wang, J.: On the trainability, stability, representability, and realizability of artificial neural networks. Ph.D. thesis, Case Western Reserve University (1991)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Data Management Systems. Morgan Kaufmann, San Francisco (2005)
Acknowledgments
This work has been partially subsidized by TIN2011-28956-C02-02 and TIN2014-55894-C2-R projects of the Spanish Inter-Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P11-TIC-7528 project of the “Junta de Andalucía” (Spain).
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Tallón-Ballesteros, A.J., Riquelme, J.C., Ruiz, R. (2016). Accuracy Increase on Evolving Product Unit Neural Networks via Feature Subset Selection. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_12
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DOI: https://doi.org/10.1007/978-3-319-32034-2_12
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