Abstract
In a Feature Subset Selection (FSS) problem, the objective is to obtain an optimal feature subset on which the learning algorithm can focus and neglect the irrelevant features. A wrapper formulates the FSS as a combinatorial optimization problem. In this paper, we propose an elitist quantum inspired Differential Evolution (QDE) algorithm for FSS. The performance of QDE is found to be significantly better than that of Binary Differential Evolution (BDE) algorithm on three benchmark problems taken from literature. In both cases, logistic regression was chosen as the classifier. Further, QDE outperformed not only the extant algorithms reported in literature but also the t-statistic cum logistic regression based filter method for FSS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kohavi, R., John, G.: Wrappers for feature selection. Artif. Intell. 1–2, 273–324 (1997)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997). Kluwer Academic Publishers
Storn, R., Price, K.: Differential Evolution-a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95–012, ICSI, ftp.icsi.berkeley.edu (1995)
Engelbrecht, A., Pampara, G.: Binary differential evolution strategies. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1942–1947 (2007)
Peng, C., Jian, L., Zhiming, L.: Solving 0-1 knapsack problems by a discrete binary version of differential evolution. In: Second International Symposium on Intelligent Information Technology Application, pp. 513–516 (2008)
Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th ACM Symposium on Theory of Computing, pp.212–219 (1996)
Shor, P.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26, 1484–1509 (1997)
Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6(6), 580–593 (2002)
Sun, J., Feng, B., Xu, W.B.: Particle swarm optimization with particles having quantum behavior. In: proceedings of the IEEE Congress on Evolutionary Computation, pp. 325–331 (2004)
Chen, H., Zou, B.: Optimal feature selection algorithm based on quantum-inspired clone genetic strategy in text categorization. In: Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation (2009)
Draa, A., Meshoul, S., Talbi, H., Batouche, M.: A quantum inspired differential evolution algorithm for solving the N-queens problem. Int. Arab J. Inf. Technol. 7(1), 21–27 (2010)
Hamed, H.N.A., Kasabov, N.K., Shamsuddin, S.M.: Quantum inspired particle swarm optimization for feature selection and parameter optimization in evolving spiking neural networks for classification tasks. In: Evolutionary Algorithms, vol. 1, pp. 132–148 (2011)
Hota, A.R., Pat, A.: An adaptive quantum-inspired differential evolution algorithm for 0-1 knapsack problem. Comput. Sci. Neural Evol. Comput. 1, 2–8 (2011)
Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.A., Moroz, I.M.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed. Eng. Online 6(1), 23 (2007)
Lichman, M.: UCI Machine Learning Repository. [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science (2013)
Street, W.N., Wolberg, W.H., Mangasarian, O.L.: Nuclear feature extraction for breast tumor diagnosis. In: International Symposium on Electronic Imaging: Science and Technology, IS&T/SPIE 1993, vol. 1905, pp. 861–870, San Jose, CA (1993)
Tsanas, A., Little, M.A., McSharry, P.E., Ramig, L.O.: Accurate telemonitoring of parkinsons disease progression using non-invasive speech tests. IEEE Trans. Biomed. Eng. 57, 884–893 (2010)
Sigillito, V.G., Wing, S.P., Hutton, L.V., Baker, K.B.: Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Tech. Digest 10, 262–266 (1989)
Liu, H., Li, J., Wong, L.: A comparative study on feature selection & classification methods using gene expression profiles and proteomic patterns. Genome Inform. 13, 51–60 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Srikrishna, V., Ghosh, R., Ravi, V., Deb, K. (2015). Elitist Quantum-Inspired Differential Evolution Based Wrapper for Feature Subset Selection. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-26181-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26180-5
Online ISBN: 978-3-319-26181-2
eBook Packages: Computer ScienceComputer Science (R0)