Abstract
Feature selection has been applied in several areas of science and engineering for a long time. This kind of pre-processing is almost mandatory in problems with huge amounts of features which requires a very high computational cost and also may be handicapped very frequently with more than two classes and lot of instances. The general taxonomy clearly divides the approaches into two groups such as filters and wrappers. This paper introduces a methodology to refine the feature subset with an additional feature selection approach. It reviews the possibilities and deepens into a new class of algorithms based on a refinement of an initial search with another method. We apply sequentially an approximate procedure and an exact procedure. The research is supported by empirical results and some guidelines are drawn as conclusions of this paper.
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References
Bache, K., Lichman, M.: UCI machine learning repository (2013)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems, vol. 1. Oxford University Press, Oxford (1999)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Hoboken (1991)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life 5(2), 137–172 (1999)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26(1), 29–41 (1996)
Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, pp. 227–263. Springer, Boston (2010)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)
Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)
Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of the 2003 UK Workshop on Computational Intelligence, vol. 1 (2003)
Kennedy, J.: Swarm intelligence. In: Zomaya, A.Y. (ed.) Handbook of Nature-inspired and Innovative, pp. 187–219. Springer, Heidelberg (2006)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the Ninth International Conference on Machine Learning (ICML 1992), pp. 249–256, San Francisco, CA. Morgan Kaufmann (1992)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)
Liu, H., Setiono, R.: A probabilistic approach to feature selection - a filter solution. In: Proceedings of the Thirteenth International Conference on Machine Learning (ICML 1996), pp. 319–327, Italy. Morgan Kaufmann (1996)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine learning, neural and statistical classification (1994)
Quinlan, J.R.: C4. 5: Programming for Machine Learning. Morgan Kauffmann, Burlington (1993)
Tallón-Ballesteros, A.J., Riquelme, J.C.: Tackling ant colony optimization meta-heuristic as search method in feature subset selection based on correlation or consistency measures. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds.) IDEAL 2014. LNCS, vol. 8669, pp. 386–393. Springer, Cham (2014). doi:10.1007/978-3-319-10840-7_47
Tallón-Ballesteros, A.J., Riquelme, J.C.: Low dimensionality or same subsets as a result of feature selection: an in-depth roadmap. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2017. LNCS, vol. 10338, pp. 531–539. Springer, Cham (2017). doi:10.1007/978-3-319-59773-7_54
Tallón-Ballesteros, A.J., Riquelme, J.C., Ruiz, R.: Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks. Connection Sci. 28(3), 242–257 (2016)
Vapnik, V.N.: The nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Witten, I.H., Frank, E., Trigg, L.E., Hall, M.A., Holmes, G., Cunningham, S.J.: Weka: practical machine learning tools and techniques with java implementations (1999)
Acknowledgments
This work has been partially subsidised by TIN2014-55894-C2-R project of the Spanish Inter-Ministerial Commission of Science and Technology (MICYT), FEDER funds and P11-TIC-7528 project of the “Junta de Andalucía” (Spain).
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Tallón-Ballesteros, A.J., Correia, L., Cho, SB. (2017). Stochastic and Non-Stochastic Feature Selection. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_64
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DOI: https://doi.org/10.1007/978-3-319-68935-7_64
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