Abstract
Expression of a gene at a molecular level can lead to the diagnosis and prediction of diseases especially cancer. In microarray datasets, large amount of genes exists and this makes the classification process complex. Since the selected genes must contain enough number of features for classification, selecting suitable features for classification is crucial. Due to the complexity of the problem, it is accepted as a NP-hard problem and an evolutionary approach; Clonal Selection Algorithm (CLONALG) is chosen to produce solution for this problem. In this paper, an Improved Clonal Selection Algorithm (ICSAT) with K-nearest neighbor (K-NN) method are used together to select the relevant features of genes for an accurate gene classification. The proposed method ICSAT-KNN is tested on three gene datasets and compared with two existing algorithms. The obtained classification accuracy values are quite competitive or even better than the values of the compared algorithms. Experimental results show that ICSAT-KNN method can serve as a reliable tool for feature selection and accurate data classification.
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References
Ghosh, M., Begum, S., Sarkar, R., Chakraborty, D., Maulik, U.: Recursive memetic algorithm for gene selection in microarray data. Expert Syst. Appl. 116, 172–185 (2019)
Ving, D., Lam, C.: Gene selection using a hybrid memetic and nearest shrunken centroid algorithm. In: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 190–197, Rome (2016)
Cai, R., Hao, Z., Yang, X., Wen, W.: An efficient gene selection algorithm based on mutual information. Neurocomputing 72(4–6), 991–999 (2009)
Alshamlan, H.M., Badr, G.H., Alohali, Y.A.: Genetic bee colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Comput. Biol. Chem. 56, 49–60 (2015)
Alshamlan, H.M., Badr, G.H., Alohali, Y.A.: mRMR-ABC: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. Biomed. Res. Int. 2015, 15 (2015)
Prasad, Y., Biswas, K.K., Hanmandlu, M.: A recursive PSO scheme for gene selection in microarray data. Appl. Soft Comput. 71, 213–225 (2018)
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(1), 29–38 (2008)
Li, S., Wu, X., Tan, M.: Gene selection using hybrid particle swarm optimization and genetic algorithm. Soft. Comput. 12(11), 1039–1048 (2008)
De Castro, L.N., Von Zuben, F.J.: The clonal selection algorithm with engineering applications. In: Proceedings of GECCO, pp. 36–39, Nevada (2000)
Babayigit, B., Akdagli, A., Guney, K.: A clonal selection algorithm for null synthesizing of linear antenna arrays by amplitude control. J. Electromagn. Wave 20, 1007–1020 (2002)
Gao, S., Dai, H., Yang, G., Tang, Z.: A novel clonal selection algorithm and its application to traveling salesman problem. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 90(10), 2318–2325 (2007)
Deniz, E., Ülker, S.: Clonal selection algorithm application to simple microwave matching network. Microw. Opt. Technol. Lett. 53(5), 991–993 (2011)
De Castro, L.N., Von Zuben, F.J.: An evolutionary immune network for data clustering. In: Neural Networks Proceedings of Sixth Brazilian Symposium, pp. 84–89, Rio De Janeiro (2000)
Ülker, E.D.: An improved clonal selection algorithm using a tournament selection operator and its application to microstrip coupler design. Turkish J. Electr. Eng. Comput. Sci. 25(3), 1751–1761 (2017)
Blickle, T., Thiele, L.: A mathematical analysis of tournament selection. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 9–16, Pittsburgh (1995)
Blickle, T.: Tournament selection. Evol. Comput. 1, 181–186 (2000)
Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection, and the effects of noise. Complex Syst. 9, 193–212 (1995)
Li, G., Wang, Q., Du, Q.: Improved harmony search algorithms by tournament selection operator. In: IEEE Congress on Evolutionary Computations, pp. 3116–3123, Sendai (2015)
Qu, B.Y., Suganthan, P.N.: Novel multimodal problems and differential evolution with ensemble of restricted tournament selection. In: IEEE Congress on Evolutionary Computation, pp. 1–7, Barcelona (2010)
Angeline, P.J.: Using selection to improve particle swarm optimization. In: IEEE International Conference on Evolutionary Computation Proceedings, pp. 84–89, Alaska (1998)
Fix, E., Hodges, J.L.: Discriminatory analysis-nonparametric discrimination: consistency properties. California University Berkeley (1951)
Statnikov, A., Aliferis, C.F., Tsamardinos, I., Hardin, D., Levy, S.: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5), 631–643 (2004)
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Ülker, E.D. (2020). Gene Selection in Microarray Data Using an Improved Approach of CLONALG. In: Hemanth, D., Kose, U. (eds) Artificial Intelligence and Applied Mathematics in Engineering Problems. ICAIAME 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-36178-5_36
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DOI: https://doi.org/10.1007/978-3-030-36178-5_36
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