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BFSSGA: Enhancing the Performance of Genetic Algorithm using Boosted Filtering Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 51 - Number 19 |
| Year of Publication: 2012 |
| Authors: Shaikh Jeeshan Kabeer, Moin Mahmud Tanvee, Md Arifur Rahman, Abdul Mottalib, Md. Hasanul Kabir |
10.5120/8153-1927
|
Shaikh Jeeshan Kabeer, Moin Mahmud Tanvee, Md Arifur Rahman, Abdul Mottalib, Md. Hasanul Kabir . BFSSGA: Enhancing the Performance of Genetic Algorithm using Boosted Filtering Approach. International Journal of Computer Applications. 51, 19 ( August 2012), 29-34. DOI=10.5120/8153-1927
Abstract
Modern microarray chips can hold gene information from thousands of genes and hundreds of individuals and the main challenge of an effective feature selection method is to identify most useful genes from the whole dataset. Removal of less informative genes helps to alleviate the effects of noise and redundancy and simplifies the task of disease classification and prediction of medical conditions such as cancer. Genetic Algorithm (GA) based wrapper model performs well but suffers from over-fitting problem and the initial population is large and random. Traditional approaches use a filter based preprocessing step to reduce the dimension of the data on which GA operates and as filtering methods on its own has shown to introduce redundant features, in this paper Boosted Feature Subset Selection (BFSS) which is a boosted t-score filter method, is used as a preprocessing step. The gene subset provided by BFSS is fed to a Genetic Algorithm which reduces the feature subset in smaller numbers and helps to generate a better optimal subset of genes. The proposed hybrid approach is applied on leukemia, colon and lung cancer benchmarked datasets and have shown better results than other well-known approaches.
References
- Li L, Jiang W, Li X, Moser KL, Guo Z, Du L, Wang Q, Topol EJ, Wang Q, Rao S, "A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset", Genomics, vol 85, pp 16-23, 2005.
- XianXu, Aidong Zhang, "Boost Feature Subset Selection: A New Gene Selection Algorithm for Microarray Dataset", International Conference on Computational Science 2006 (ICCS 2006), Lecture Notes in Computer Science, vol. 3992/2006, pp 670-677, 2006.
- T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. GaasenBeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Blom?eld, E. S. Lander, "Molecular classi?cation of cancer: class discovery and class prediction by gene-expression monitoring, Science", vol. 286, 531–537, 1999.
- Shital Shah, Andrew Kusiak, "Cancer gene search with data-mining and genetic algorithms", Computers in Biology and Medicine, vol. 37, pp 251-261, 2007.
- P. J. Park, M. Pagano, M. Bonetti, "A nonparametric scoring algorithm for identifying informative genes from microarray data", Pac. Symp. Biocomput. , pp 52–63, 2001.
- T. Jirapech-Umpai, S. Aitken "Feature selection and classi?cation for microarray data analysis: evolutionary methods for identifying predictive genes", BMC Bioinformatics, vol. 6, 2005.
- L. Li, C. Weinberg,T. Darden, L. Pedersen, "Gene selection for sample classi?cation based on gene expression data : study of sensitivity to choice of parameters of the GA/KNN method", Bioinformatics, vol. 17 pp 1131–1142, 2001.
- YvanSaeys, I˜nakiInza and Pedro Larra˜naga, "A review of feature selection techniques in bioinformatics", Oxford Journals – Bioformatics, vol. 23, pp 2507-2517, June 2007.
- MohdSaberiMohamad, SigeruOmatu, SafaaiDeris, Michifumi Yoshioka, "Selecting Informative Genes from Microarray Data by Using a Cyclic GA-based Method", 2010 International Conference on Intelligent Systems, Modelling and Simulation, pp 15-20, Kuala Lampur, Jan 2010.
- Mitchell Melanie, "An Introduction to Genetic Algorithms", MIT Press, 1999.
- Haupt,R. L. and Haupt,S. E, "Practical Genetic Algorithms", Wiley, 1998.
- Ooi CH, Tan P, "Genetic algorithms applied to multi-class prediction for the analysis of gene expression data", Oxford University Press, vol. 19 no 1, pp 37-44, 2003.
- G M. S. Mohamad, S. Omatu, S. Deris, M. F. Misman, and M. Yoshioka, "A Multi-objective Strategy in Genetic Algorithm for Gene Selection of Gene Expression Data," International Journal of Artificial Life & Robotics, vol. 13, no. 2, pp. 410-413, 2009.
- H. L. Huang, F. L. Chang, "ESVM: Evolutionary Support Vector Machine for Automatic Feature Selection and Classification of Microarray Data," Biosystems, vol. 90, pp. 516-528, 2007.
- S. Peng, Q. Xu, X. B. Ling, X. Peng, W. Du, and L. Chen, "Molecular Classification of Cancer Types from Microarray Data Using the Combination of Genetic Algorithms and Support Vector Machines," FEBS Letters, vol. 555, pp. 358-362, 2003.
- Huijuan Lu, Wutao Chen, Xiaoping Ma, Mingyi Wang, Jinwei Zhang , "Model-free Gene Selection Using Genetic Algorithms ", JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 5, No. 1, pp. 195-203, 2011.
- Feng Tan, Xuezheng Fu, Yanqing Zhang AnuG. Bourgeois, "A genetic algorithm-based method for feature subset selection", Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 12, pp 111-120, September 2007.
- LaetitiaJourdan, Clarisse Dhaenens, El-GhazaliTalbi, " A Genetic Algorithm for Feature Selection in Data-Mining for Genetics", 4th Metaheuristics International Conference [MIC'2001 Porto], Portugal, July 2001.
- Chien-Pang Lee,YunghoLeu, "A novel hybrid feature selection method for microarray data analysis", Applied Soft Computing, vol. 11, pp 208–213, 2009.
- Isabelle Guyon, Andr´ eElisseeff, "An Introduction to Variable and Feature Selection", Journal of Machine Learning Research, vol. 3, pp 1157-1182, 2003.
- K. E. Parsopoulos, D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos and M. N. Vrahatis, "Vector evaluated differential evolution for multiobjective optimization", In Congress on Evolutionary Computation (CEC 2004), Portland, Oregon, pp. 204-211, 2004.
- Rich Caruana, Virginia R. de Sa, "Benefitting from the Variables that Variable Selection Discards", Journal of Machine Learning Research, vol. 3, pp 1245-1264, 2003.
- ThanyalukJirapech-Umpai, Stuart Aitken, "Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes", BMC Bioinformatics, vol. 6:148, [Online], 2005.
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