Abstract
A successful interpretation of data goes through discovering crucial relationships between variables. Such a task can be accomplished by a Bayesian network. The dark side is that, when lots of variables are involved, the learning of the network slows down and may lead to wrong results. In this study, we demonstrate the feasibility of applying an existing Particle Swarm Optimization (PSO)-based approach to feature selection for filtering the irrelevant attributes of the dataset, resulting in a fine Bayesian network built with the K2 algorithm. Empirical tests carried out with real data coming from the bioinformatics domain bear out that the PSO fitness function is in a straight concordance to the most widely known validation measures for classification.
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References
Baldi, P., Brunak, S.: Assessing the accuracy of prediction Algorithms for classification: An Overview. Bioinformatics 16(5), 412–424 (2000)
Bockhorst, J., Craven, M., Page, D., Shavlik, J., Glasner, J.: A Bayesian network approach to operon prediction. Bioinformatics 19(10), 1471227–1471235 (2003)
Bouckaert, R.R.: Bayesian Network Classifiers in Weka (2004)
Brazma, A., Jonassen, I.: Context - specific independence in Bayesian networks. In: Proc. Twelfth Conference on Uncertainty in Artificial Intelligence, pp. 115–123 (1996)
Grau, R., Galpert, D., Chávez, M., Sánchez, R., Casas, G., Morgado, E.: Algunas aplicaciones de la estructura booleana del Código Genético. Revista Cubana de Ciencias Informáticas, Año 1, vol 1 (2005)
Hunter, L.: Artificial Intelligence and Molecular Biology. p. 500, references, index, illus. electronic text, http://www.biosino.org/mirror/www.aaai.org/Press/Books/Hunter/default.htm ISBN 0-262-58115-9
Marchal, K., Thijs, G., De Keersmaecker, S., Monsieurs, P., De Moor, B., Vanderleyden, J.: Genome-specific higher-order background models to improve motif detection. Trends in Microbiology 11(2), 61–66 (2003)
Mahamesd, G.H.O., Andries, P.E., Ayed, S.: Dynamic Clustering using PSO with Application in Unsupervised Image Classification. Transactions on Engineering, computing and Technology 9 (2005)
Kennedy, J.: The particle swarm: social adaptation of knowledge. In: IEEE International Conference on Evolutionary Computation, April 13–16, 1997, pp. 303–308 (1997)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Computer Society Press, Los Alamitos (1995)
Kennedy, J., Eberhart, R.C.: A new optimizer using particle swarm theory. In: Sixth International Symposium on Micro Machine and Human Science, Nagoya, pp. 39–43 (1995)
Kennedy, J., Spears, W.M.: Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 39–43. IEEE Computer Society Press, Los Alamitos (1998)
Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33(1), 25–41 (2000)
Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J.A., Armañanzas, R., Santafé, G., Pérez, G., Robles, V.: Machine learning in bioinformatics. BIOINFORMATICS 7(1), 86–112 (2005)
Liu, H., Li, J., Wong, L.: A Comparative Study on Feature Selection and Classification Methods Using Gene Expression Profiles and Proteomic Patterns. Genome Informatics 13, 51–60 (2002)
Liu, H., Setiono, R.: Chi2: Feature selection and discretization of numeric attributes. In: Proc. IEEE 7th International Conference on Tools with Artificial Intelligence, pp. 338–391. IEEE Computer Society Press, Los Alamitos (1995)
Mellors, J.W., Brendan, A.L., Schinazi, R.F.: Mutations in HIV-1 Reverse Transcriptase and Protease Associated with Drug Resistance
Murray, R.J.: Predicting Human Immunodeficiency Virus Type 1 Drug Resistance From Genotype Using Machine Learning. Master of Science School of Informatics. University of Edinburgh (2004)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishing, Dordrecht (1991)
Sánchez, R., Grau, R., Morgado, E.: Genetic Code Boolean Algebras. WSEAS Transactions on Biology and Biomedicine 1(2), 190–197 (2004)
Sánchez, R., Morgado, E., Grau, R.: A genetic code boolean structure I. The meaning of boolean deductions. Bulletin of Mathematical Biology 67, 1–14 (2005)
Sánchez, R., Morgado, E., Grau, R.: The genetic code boolean lattice, MATCH Communications in Mathematical and in Computer Chemistry, vol. 52, pp. 29–46 (2004)
Siegel, S.: Diseño Experimental no parametrico, segunda edicion, 245–256 (1987)
Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature Selection Based on Rough Sets and Particle Swarm Optimization. In: Pattern Recognition Letter, Elsevier, Amsterdam (2006)
Witten, I.H., Frank, E.: Data Mining Practical Machine Learning Tools and Techniques, pp. 363–483. Morgan Kaufman, San Francisco (2005)
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Chávez, M.d.C., Casas, G., Falcón, R., Moreira, J.E., Grau, R. (2007). Building Fine Bayesian Networks Aided by PSO-Based Feature Selection. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_42
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DOI: https://doi.org/10.1007/978-3-540-76631-5_42
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