Abstract
An elitist model of Binary Particle Swarm Optimization (BP SO) algorithm is proposed for feature selection from high dimensional data. Since the data are highly redundant, a fast pre-processing algorithm is employed to reduce features from high dimensions in a crude manner. The reduced feature subsets being still high dimensional, a further reduction is achieved by the proposed algorithm. The non-dominated sorting PSO algorithm is performed on the combined solutions of each two successive generations that also help to preserve the best solutions in a generation. The fitness functions are suitably formulated in multi objective framework for the conflicting objectives, i.e., to reduce the cardinality of the feature subsets and to increase the accuracy. The performance of the proposed algorithm is demonstrated on three high dimensional benchmark datasets, i.e., colon cancer, lymphoma and leukemia data.
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References
Lazar, C., et al.: A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9(4), 1106–1119 (2012)
Inza, I., Saeys, P.L.Y.: A review of feature selection techniques in Bioinformatics. International Journal of Computer Science (IAENG) 23(19), 2507–2517 (2007)
ElAlami, M.E.: A filter model for feature subset selection based on genetic algorithm. Knowledge-Based Systems 22(5), 356–362 (2009)
Sainin, M.S., Alfred, R.: A genetic based wrapper feature selection approach using nearest neighbour distance matrix. In: 2011 3rd Conference on Data Mining and Optimization (DMO), pp. 237–242 (2011)
Wahid, C.M.M., Ali, A.B.M.S., Tickle, K.S.: A novel hybrid approach of feature selection through feature clustering using microarray gene expression data. In: 2011 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 121–126 (2011)
Nagi, S., Bhattacharyya, D.: Classification of microarray cancer data using ensemble approach. Network Modeling Analysis in Health Informatics and Bioinformatics, 1–15 (2013)
Mladenič, D.: Feature selection for dimensionality reduction. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 84–102. Springer, Heidelberg (2006)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, 1997 Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997)
Deb, K.: Multi-objective optimization. Multi-objective Optimization Using Evolutionary Algorithms, 13–46 (2001)
Banerjee, M., Mitra, S., Banka, H.: Evolutionary rough feature selection in gene expression data. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(4), 622–632 (2007)
Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proc. IEEE Congress, vol. 3, pp. 1945–1950 (1999)
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Dara, S., Banka, H. (2014). An Elitist Binary PSO Algorithm for Selecting Features in High Dimensional Data. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_78
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DOI: https://doi.org/10.1007/978-3-319-07353-8_78
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07352-1
Online ISBN: 978-3-319-07353-8
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