Abstract
This paper presents the use of a bi-objective genetic algorithm to select attributes for an ensemble system. This is achieved by using this technique to simultaneously maximize the individual diversity of the base classifiers and the group diversity of an ensemble system. In order to evaluate the possible solutions obtained by this technique, two filter-based evaluation criteria will be used. Filter-based criteria were chosen because they are independent of the learning algorithm and have a low computational cost.
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References
Yu, S.: Feature selection and classifier ensembles: A study on hyperspectral remote sensing data. PhD thesis. University of Antwerp, Antwerp (2003)
Huang, C., Wang, C.: A ga-based feature selection and parameters optimizationfor support vector machines. Expert Systems with App. 31(2), 231–240 (2006)
Opitz, D.W.: Feature selection for ensembles. In: Conference on Artificial Intelligence, AAAI. American Association for Artificial Intelligence, pp. 379–384 (1999)
Canuto, A.M.P., Abreu, M.C.C., de Melo Oliveira, L., Xavier Jr., J.C., de M. Santos, A.: Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recognition Letters 28(4), 472–486 (2007)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley Interscience (2004)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Durillo, J., Nebro, A., Luna, F., Dorronsoro, B., Alba, E.: jmetal: a java framework for developing multi-objective optimization metaheuristics. Department of Languages and Computer Sciences, Tech. Rep. (2006)
Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm. Department of Electrical Engineering, Swiss Federal Institute of Technology, Tech. Rep. (2001)
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Santana, L.E.A., Canuto, A.M.P. (2012). Bi-objective Genetic Algorithm for Feature Selection in Ensemble Systems. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_88
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DOI: https://doi.org/10.1007/978-3-642-33269-2_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33268-5
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