Abstract
Gene expression data have been analyzing by many researchers by using a range of computational intelligence methods. From the gene expression data, selecting a small subset of informative genes can do cancer classification. Nevertheless, many of the computational methods face difficulties in selecting small subset since the small number of samples needs to be compared to the huge number of genes (high-dimension), irrelevant genes and noisy genes. Hence, to choose the small subset of informative genes that is significant for the cancer classification, an enhanced binary particle swarm optimization is proposed. Here, the constraint of the elements of particle velocity vectors is introduced and a rule for updating particle’s position is proposed. Experiments were performed on five different gene expression data. As a result, in terms of classification accuracy and the number of selected genes, the performance of the introduced method is superior compared to the conventional version of binary particle swarm optimization (BPSO). The other significant finding is lower running times compared to BPSO for this proposed method.
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References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104–4108 (1997)
Knudsen, S.: A Biologist’s Guide to Analysis of DNA Microarray Data. John Wiley & Sons, New York (2002)
Li, S., Wu, X., Tan, M.: Gene selection using hybrid particle swarm optimization and genetic algorithm. Soft Comput. 12, 1039–1048 (2008)
Mohamad, M.S., Omatu, S., Deris, S., Misman, M.F., Yoshioka, M.: Selecting informative genes from gene expression data by using hybrid methods for cancer classification. Int. J. Artif. Life & Rob. 13(2), 414–417 (2009)
Mohamad, M.S., Omatu, S., Yoshioka, M., Deris, S.: A cyclic hybrid method to select a smaller subset of informative genes for cancer classification. Int. J. Innovative Comput., Inf. & Control 5(8), 2189–2202 (2009)
Shen, Q., Shi, W.M., Kong, W.: Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Comput. Biol. & Chem. 32, 53–60 (2008)
Chuang, L.Y., Chang, H.W., Tu, C.J., Yang, C.H.: Improved binary PSO for feature selection using gene expression data. Comput. Biol. & Chem. 32, 29–38 (2009)
Chuang, L.Y., Yang, C.H., Yang, C.H.: Tabu search and binary particle swarm optimization for feature selection using microarray data. J. Comput. Biol. 16(12), 1689–1703 (2009)
Chuang, L.Y., Yang, C.H., Li, J.C., Yang, C.H.: A hybrid BPSO-CGA approach for gene selection and classification of microarray data. J. Comput. Biol. 18, 1–14 (2011)
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Mohamad, M.S., Omatu, S., Deris, S., Yoshioka, M. (2013). A Constraint and Rule in an Enhancement of Binary Particle Swarm Optimization to Select Informative Genes for Cancer Classification. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_15
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DOI: https://doi.org/10.1007/978-3-642-40319-4_15
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