Abstract
In this paper, an improved method for cancer classification based on particle swarm optimization (PSO) and priorinformation is proposed. Firstly, the proposed method uses PSO to implement gene selection. Then, the global search algorithm such as PSO is combined with the local search one such as backpropagation (BP) to model the classifier. Moreover, the prior information extracted from the data is encoded in PSO for better performance. The proposed approach is validated on two publicly available microarray data sets. The experimental results verify that the proposed method selects fewer discriminative genes with comparable performance to the traditional classification approaches.
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References
Wang, H.-Q., Wong, H.-S., Zhu, H., Yip Timothy, T.C.: A neural network-based biomarker association information extraction approach for cancer classification. Journal of Biomedical Informatics 42, 654–666 (2009)
Huerta, E.B., Duval, B., Hao, J.-K.: A hybrid LDA and genetic algorithm for gene selection and classification of microarray data. Neurocomputing 73, 2375–2383 (2010)
Da, Y., Xiurun, G.: An improved PSO-based ANN with simulated annealing technique. Neurocomputing 63, 527–533 (2005)
Geethanjali, M., Mary Raja Slochanal, S., Bhavani, R.: PSO trained ANN-based differential protection scheme for power transformers. Neurocomputing 71, 904–918 (2008)
Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for classification of tumors using gene expression data. J. Am. Statistical Assoc. 97, 77–87 (2002)
Riget, J., Vesterstorm, J.S.: A diversity-guided particle swarm optimizer – the arPSO. EVAlife Technical Report 2:1-13 (2002)
Shi, Y.H., Eberhat, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)
Almeida, L.M., Ludermir, T.B.: A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks. Neurocomputing 73, 1438–1450 (2010)
Huawen, L., Lei, L., Huijie, Z.: Ensemble gene selection by grouping for microarray data classification. Journal of Biomedical Informatics 43, 81–87 (2010)
Fei, H., Ling, Q.-H.: A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information. Applied Mathematics and Computation 205, 792–798 (2008)
Ruichu, C., Zhifeng, H., Xiaowei, Y.: An efficient gene selection algorithm based on mutual information. Neurocomputing 72, 991–999 (2009)
Zhenyu, W., Vasile, P., Yong, X.: Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. In: International Symposium on Evolving Fuzzy Systems, vol. 241, pp. 241–246 (2006)
Lee, K.E., Sha, N., Dougherty, E.R., Vannucci, M., Mallick, B.K.: Gene selection: A Bayesian variable selection approach. Bioinformatics 19, 90–97 (2003)
Ding, S.-C., Juan, L., Qing, Y.: A GA-based optimal gene subset selection method. Machine Learning and Cybernetics 5, 2784–2789 (2004)
Cawley, G.C., Talbot, N.L.C.: Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Bioinformatics 22, 2348–2355 (2006)
Xixian, W.: Gene selection and cancer classification based on optimization algorithm and support vector machine. A thesis submitted in partial satisfaction of the Requirements for the degree of Master of Science in Control Science and Engineering in the Graduate School of Hunan University, B.E. 1-65 (2005) (in chinese)
Jiexun, L., Hua, S., Hsinchun, C.: Optimal search-based gene aubset selection for gene array cancer classification. IEEE Transactions on Information Technology in Biomedicine 11, 398–405 (2007)
Nguyen, D.V., Rocke, D.M.: Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18, 39–50 (2002)
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Han, F., Wu, YQ., Cui, Y. (2014). A Hybrid Approach for Cancer Classification Based on Particle Swarm Optimization and Prior Information. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_40
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DOI: https://doi.org/10.1007/978-3-319-11857-4_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11856-7
Online ISBN: 978-3-319-11857-4
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