Abstract
Evolutionary algorithms have received much attention in extracting knowledge on high-dimensional micro-array data, being crucial to their success a suitable definition of the search space of the potential solutions. In this paper, we present an evolutionary approach for selecting informative genes (features) to predict and diagnose cancer. We propose a procedure that combines results of filter methods, which are commonly used in the field of data mining, to reduce the search space where a genetic algorithm looks for solutions (i.e. gene subsets) with better classification performance, being the quality (fitness) of each solution evaluated by a classification method. The methodology is quite general because any classification algorithm could be incorporated as well a variety of filter methods. Extensive experiments on a public micro-array dataset are presented using four popular filter methods and SVM.
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Peng, S., et al.: Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Letter 555(2), 358–362 (2003)
Huerta, E.B., Duval, B., Hao, J.K.: A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 34–44. Springer, Heidelberg (2006)
Tan, F., Fu, X., Zhang, T., Bourgeois, A.G.: Improving Feature Subset Selection Using a Genetic Algorithm for Microarray Gene Expression Data. In: IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, July 16-21 (2006)
Dessì, N., Pes, B.: An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification. Journal of Artificial Evolution and Applications 2009, Article ID 803973, 10 pages, doi:10.1155/2009/803973
Li, L., Weinberg, C.R., Darden, T.A., Pedersen, L.G.: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17(12), 1131–1142 (2001)
Bevilacqua, V., et al.: Genetic Algorithms and Artificial Neural Networks in Microarray Data Analysis: a Distributed Approach. Engineering Letters 13(3), EL_13_3_14 (2006)
Reddy, A.R., Deb, K.: Classification of two-class cancer data reliably using evolutionary algorithms. Technical Report. KanGAL (2003)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML 1997 (1997)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research (2003)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier, Amsterdam (2005)
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Cannas, L.M., Dessì, N., Pes, B. (2010). A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data. In: Shi, Z., Vadera, S., Aamodt, A., Leake, D. (eds) Intelligent Information Processing V. IIP 2010. IFIP Advances in Information and Communication Technology, vol 340. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16327-2_36
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DOI: https://doi.org/10.1007/978-3-642-16327-2_36
Publisher Name: Springer, Berlin, Heidelberg
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