Abstract
A classification problem is fully partitioned into several small problems each of which is responsible for solving a fraction of the original problem. In this paper, a new approach using class-based partitioning is proposed to improve the performance of genetic-based classifiers. Rules are defined with fuzzy genes to represent variable length rules. We experimentally evaluate our approach on four different data sets and demonstrate that our algorithm can improve classification rate compared to normal Rule-based classification GAs [1] with non-partitioned techniques.
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
DeJong, K., Spears, W.: Learning concept classification rules using genetic algorithms. In: International Joint Conference on Artificial Intelligence, pp. 651–656 (1991)
Corcoran, A., Sen, S.: Using real-valued genetic algorithm to evolve rule sets for classification. In: First IEEE Conference on Evolutionary Computation, Orlando, USA, pp. 120–124 (1994)
Ishibuchi, H., Nakashima, T., Murata, T.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man and Cybernetics, Part B 29, 601–618 (1999)
Weiss, S., Kulikowski, C.: Computer Systems that Learn Classification and Prediction Methods from Statistics. Neural Nets, Machine Learning, and Expert Systems., Morgan Kaufmann Publishers, San Mateo (1991)
Chang, Y.H., Zeng, B., Wang, X.H., Good, W.F.: Computer-aided diagnosis of breast cancer using artificial neural networks: Comparison of backpropagation and genetic algorithms. In: International Joint Conference on Neural Networks, Washington, DC, pp. 3674–3679 (1999)
Fidelis, M.V., Lopes, H.S., Freitas, A.A.: Discovering comprehensible classification rules with a genetic algorithm. In: Proceedings of Congress on Evolutionary Computation (2000)
Ghosh, A., Nath, B.: Multi-objective rule mining using genetic algorithms. Information Science 163, 123–133 (2004)
Tan, K.C., Yu, Q., Heng, C.M., Lee, T.H.: Evolutionary computing for knowledge discovery in medical diagnosis. Artificial Intelligence in Medicine 27, 129–154 (2003)
Merelo, J., Prieto, A., Moran, F.: Optimization of classifiers using genetic algorithms. In: Patel, M., Honavar, V., Balakrishnan, K. (eds.) Advances in the Evolutionary Synthesis of Intelligent Agents. MIT Press, Cambridge (2001)
Michie, D.: Problem decomposition and the learning of skills, pp. 17–31. Springer, Berlin (1995)
Guan, S., Li, S.: Parallel growing and training of neural networks using output parallelism. IEEE Transactions on Neural Networks 13, 1–9 (2002)
Jenkins, R., Yuhas, B.: A simplified neural network solution through problem decomposition: the case of the truck backerupper. IEEE Transactions on Neural Networks 4, 718–720 (1993)
Lu, B., Ito, M.: Task decomposition and module combination based on class relations: a modular neural network for pattern classification. IEEE Transactions on Neural Networks 10, 1244–1256 (1999)
Rodriguez, M., Escalante, D.M., Peregrin, A.: Efficient distributed genetic algorithm for rule extraction. Applied Soft Computing 11(1), 733–743 (2011)
Rokach, L., Maimon, O.: Improving supervised learning by feature decomposition. In: The Second International Symposium on Foundations of Information and Knowledge Systems, pp. 178–196 (2002)
Weile, D., Michielssen, E.: The use of domain decomposition genetic algorithms exploiting model reduction for the design of frequency selective surfaces. Computer Methods in Applied Mechanics and Engineering, 439–458 (2000)
Masulli, F., Valentini, G.: Parallel non-linear dichotomizers. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 2, pp. 29–33 (2000)
Guan, S.U., Zhu, F.: A class decomposition approach for ga-based classifiers. Engineering Applications of Artificial Intelligence 18, 271–278 (2005)
Apte, C., Hong, S., Hosking, J., Lepre, J., Pednault, E., Rosen, B.: Decomposition of heterogeneous classification problems. In: Liu, X., Cohen, P.R. (eds.) IDA 1997. LNCS, vol. 1280, pp. 17–28. Springer, Heidelberg (1997)
Watson, R., Pollack, J.: Symbolic combination in genetic algorithms. In: Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, pp. 425–434 (2000)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, MI (1975)
Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artificial Intelligence Review 12, 265–319 (1998)
Blake, C.L., Merz, C.J.: Repository of machine learning databases (1998)
Kaya, M.: Autonomous classifiers with understandable rule using multi-objective genetic algorithms. Expert Systems with Applications 37, 3489–3494 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
El-Kafrawy, P., Sauber, A. (2011). Using Class Decomposition for Building GA with Fuzzy Rule-Based Classifiers. In: Mehrotra, K.G., Mohan, C., Oh, J.C., Varshney, P.K., Ali, M. (eds) Developing Concepts in Applied Intelligence. Studies in Computational Intelligence, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21332-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-21332-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21331-1
Online ISBN: 978-3-642-21332-8
eBook Packages: EngineeringEngineering (R0)