Abstract
The microarray data classification problem is a recent complex pattern recognition problem. The most important goal in supervised classification of microarray data, is to select a small number of relevant genes from the initial data in order to obtain high predictive classification accuracy. With the framework of a hybrid filter-wrapper, we study in this paper the role of the multi-parent recombination operator. For this purpose, we introduce a Random Multi Parent crossover (RMPX) and we analyze their effects in a genetic algorithm (GA) which is combined with Fisher’s Linear Discriminant Analysis (LDA). This hybrid algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDA’s discriminant coefficients to integrate a multi-parent specialized crossover and mutation operation to improve the performance of gene selection. In the experimental results it is observed that RPMX operator work very well by achieving lower classification error rates.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alizadeh, A., Eisen, M.B., et al.: Distinct Types of Diffuse Large (b)–Cell Lymphoma Identified by Gene Expression Profiling. J. Nature 403, 503–511 (2000)
Alon, U., Barkai, N., et al.: Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays. Proc. Nat. Acad. Sci. USA 96, 6745–6750 (1999)
Bongirwar, V.K., Agarwal, V.H., Raghuwanshi, M.M.: Multimodal Optimization Using Real Coded Self-Adaptive Genetic Algorithm. J. International Journal of Engineering Science and Technology 1, 61–66 (2011)
Ben-Dor, A., Bruhn, L., et al.: Tissue Classification with Gene Expression Profiles. J. Computational Biology 7(3-4), 559–583 (2000)
Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. J. The American Statistical Association 97(457), 77–87 (2002)
Eiben, A.E., Raue, P.E., Ruttkay, Z.: Genetic algorithms with multi-parent Recombination. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 78–87. Springer, Heidelberg (1994)
Eiben, A.E.: Multiparent Recombination in Evolutionary Computing. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing: theory and applications 2003. Natural Computing IX, pp. 175–192. Springer, Heidelberg (2003)
Golub, T., Slonim, D., et al.: MolecularClassification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. J. Science 286(5439), 531–537 (1999)
Gong, D., Ruan, X.: A New Multi-Parent Recombination Genetic Algorithm. In: Fifth World Congress on Intelligent Control and Automation, pp. 531–537. IEEE Press, New York (2004)
Garcia-Martinez, C., Lozano, M.: Evaluating a Local Genetic Algorithm as Context-Independent Local Search Operator for Metaheuristics. J. Soft Computing 14(10), 1117–1139 (2010)
Gordon, G.J., Jensen, R.V., et al.: Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gene Expression Ratios in Lung Cancer and Mesothelioma. J. Cancer Research 17(62), 4963–4967 (2002)
Muhlenbein, H., Voigt, H.M.: Gene Pool Recombination for the Breeder Genetic Algorithm. In: The Metaheuristics International Conference, pp. 19–25. Kluwer Academic Publishers, Norwell (1995)
Patel, R., Raghuwanshi, M.M.: Multi-objective Optimization Using Multi Parent Crossover Operators. J. Journal of Emerging Trends in Computing and Information Sciences 2(2), 33–39 (2010)
Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C., Liotta, L.A.: Use of Proteomic Patterns in Serum to Identify Ovarian Cancer. J. Lancet. 359(9306), 572–577 (2002)
Pomeroy, S.L., Tamayo, P., et al.: Prediction of Central Nervous System Embryonal Tumour Outcome Based on Gene Expression. J. Nature 415, 436–442 (2002)
Singh, D., Febbo, P., Ross, K., Jackson, D., Manola, J., Ladd, C., Tamayo, P., Renshaw, A., D’Amico, A., Richie, J.: Gene Expression Correlates of Clinical Prostate Cancer Behavior. J. Cancer Cell 1, 203–209 (2002)
Ting, C.K., Su, C.H., Lee, C.N.: Multi-parent Extension of Partially Mapped Crossover for Combinatorial Optimization Problems. J. Expert Systems with Applications 37(3), 1879–1886 (2010)
Ting, C.K.: On the Convergence of Multi-parent Genetic Algorithms. In: The IEEE Congress on Evolutionary Computation, pp. 396–403. IEEE Press, Edinburgh (2005)
Tsutsui, S., Ghosh, A.: A Study of the Effect of Multi-parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms. In: The IEEE World Congress on Computational Intelligence, pp. 828–833. IEEE Press, Anchorage (1998)
Cho, S.B., Won, H.H.: Cancer Classification Using Ensemble of Neural Networks with Multiple Significant Gene Subsets. Applied Intelligence 26(3), 243–250 (2007)
Ding, C., Peng, H.: Minimum Redundancy Feature Selection From Microarray Gene Expression Data. Bioinformatics and Computational Biology 3(2), 185–206 (2005)
Li, X.Q., Zeng, J.Y., Yang, M.Q.: Partial Least Squares Based Dimension Reduction with Gene Selection for Tumor Classification. In: Proceedings of IEEE 7th International Symposium on Bioinformatics and Bioengineering, pp. 1439–1444 (2007)
Li, S., Wu, X., Hu, X.: Gene Selection Using Genetic Algorithm and Support Vectors Machines. Soft Comput. 12(7), 693–698 (2008)
Liu, B., Cui, Q., Jiang, T., Ma, S.: A Combinational Feature Selection and Ensemble Neural Network Method for Classification of Gene Expression Data. BMC Bioinformatics 5:136(138), 1–12 (2004)
Pang, S., Havukkala, I., Hu, Y., Kasabov, N.: Classification Consistency Analysis for Bootstrapping Gene Selection. Neural Computing and Applications 16:527, 539 (2007)
Peng, Y., Li, W., Liu, Y.: A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data. Cancer Informatics 2, 301–311 (2006)
Tan, A.C., Gilbert, D.: Ensemble Machine Learning on Gene Expression Data for Cancer Classification. Applied Bioinformatics 2(2), 75–83 (2003)
Wang, Z., Palade, V., Xu, Y.: Neuro-fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis. In: Proc. Evolving Fuzzy Systems, pp. 241–246 (2006)
Ye, J., Li, T., Xiong, T., Janardan, R.: Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data. IEEE/ACM Trans. Comput. Biology Bioinform. 1(4), 181–190 (2004)
Yue, F., Wang, K., Zuo, W.: Informative Gene Selection and Tumor Classification by Null Space Lda for Microarray Data. In: Chen, B., Paterson, M., Zhang, G. (eds.) ESCAPE 2007. LNCS, vol. 4614, pp. 435–446. Springer, Heidelberg (2007)
Zhang, L., Li, Z., Chen, H.: An Effective Gene Selection Method Based on Relevance Analysis and Discernibility Matrix. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 1088–1095. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bonilla-Huerta, E., Duval, B., Hernández, J.C.H., Hao, JK., Morales-Caporal, R. (2012). Hybrid Filter-Wrapper with a Specialized Random Multi-Parent Crossover Operator for Gene Selection and Classification Problems. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-24553-4_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24552-7
Online ISBN: 978-3-642-24553-4
eBook Packages: Computer ScienceComputer Science (R0)