Abstract
One of the successful methods in classification problems is feature selection. Feature selection algorithms; try to classify an instance with lower dimension, instead of huge number of required features, with higher and acceptable accuracy. In fact an instance may contain useless features which might result to misclassification. An appropriate feature selection methods tries to increase the effect of significant features while ignores insignificant subset of features. In this work feature selection is formulated as an optimization problem and a novel feature selection procedure in order to achieve to a better classification results is proposed. Experiments over a standard benchmark demonstrate that applying harmony search in the context of feature selection is a feasible approach and improves the classification results.
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References
Il-S. Oh, J.S., Lee, B.R.: Moon,: Hybrid Genetic Algorithms for Feature Selection. IEEE Trans. On Pattern Analysis and Machine Intelligence 26(11) (November 2004)
Wang, X., Teng, X., Xia, W., Jensen, R.: Feature Selection Based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters 24, 459–471 (2007)
Lanzi, P.: Fast Feature Selection with Genetic Algorithms,: A Filter Approach. In: Proceeding of IEEE International Conference on Evolutionary Computation, pp. 537–540 (1997)
Sikora, R., Piramuthu, S.: Framework for Efficient Feature Selection in Genetic Algorithm-based Data Mining. European Journal of Operational research 180, 723–737 (2007)
Kabir, M., Islam, M., Murase, K.: A New Wrapper Feature Selection Approach using Neural Network. Neurocomputing 73, 3273–3283 (2010)
Gheyas, I.A., et al.: Feature Subset Selection in Large Dimensionality Domains. Pattern Recognition 43, 5–13 (2010)
Cover, T.M.: The Best two Independent Measurements are not the two Best. IEEE Trans. Systems Man Cybern. 4(2), 116–117 (1974)
Zhang, H., Sun, G.: Feature Selection using Tabu Search Method. Pattern Recognition 35, 701–711 (2002)
Garcia, F.C., Garcia Torres, M., Batista, B.M., Moreno, J.A., Marcos, P.J.: Solving Feature Subset Selection Problem by A Parallel Scatter Search. European Journal of Operational Research 169(2), 477–489 (2006)
Lee, K., Geem, Z.: A New Meta-heuristic Algorithm for Continuous Engineering Optimization. Harmony Search Theory and Practice, Computer Methods in Applied Mechanics and Engineering 194, 3902–3933 (2005)
Omra, M.G.H., Mahdavi, M.: Global-best Harmony Search. Applied mathematics and computing 198, 643–656 (2008)
Unler, A., Murat, A.: A Discrete Particle Swarm Optimization Method for Feature Selection in Binary Classification Problems. European Journal of Operation Research 206, 528–539 (2010)
Das, S., Mukhopadhyay, A., Roy, A., Abraham, A., Panigrahi, B.K.: Exploratory Power of the Harmony Search Algorithm. Analysis and Improvements for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(1), 89–106 (2011)
Mahdavi, M., et al.: An Improved Harmony Search Algorithm for Solving Optimization Problems. Applied Mathematics and Computation 188, 1567–1579 (2007)
Forsati, R., Mahdavi, M.: Web Text Mining Using Harmony Search, Recent Advances. In: Harmony Search Algorithm, pp. 51–64 (2010)
Forsati, R., Meybodi, M.R., Mahdavi, M., Ghari Neiat, A.: Hybridization of K-Means and Harmony Search Methods for Web Page Clustering. In: Web Intelligence, pp. 329–335 (2008)
Forsati, R., Mahdavi, M., Haghighat, A.T., Ghariniyat, A.: An Efficient Algorithm for Bandwidth-delay Constrained Least Cost Multicast Routing. In: Canadian Conference Electrical and Computer Engineering, CCECE 2008, pp. 1641–1646 (2008)
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Forsati, R., Moayedikia, A., Safarkhani, B. (2011). Heuristic Approach to Solve Feature Selection Problem. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22027-2_59
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DOI: https://doi.org/10.1007/978-3-642-22027-2_59
Publisher Name: Springer, Berlin, Heidelberg
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