Abstract
This article describes a way of designing a hybrid system for classification and rule generation, in soft computing paradigm, integrating rough set theory with a fuzzy MLP using an evolutionary algorithm. An l-class classification problem is split into l two-class problems. Crude subnetworks are initially obtained for each of these two-class problems via rough set theory. These subnetworks are then combined and the final network is evolved using a GA with restricted mutation operator which utilizes the knowledge of the modular structure already generated, for faster convergence. The GA tunes the fuzzification parameters, and the network weights and structure simultaneously, by optimizing a single fitness function.
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
Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Communications of the ACM 37, 77–84 (1994)
Proc. of Fifth International Conference on Soft Computing (IIZUKA 1998), Iizuka, Fukuoka, Japan (October 1998)
Fu, L.M.: Knowledge-based connectionism for revising domain theories. IEEE Transactions on Systems, Man and Cybernetics 23, 173–182 (1993)
Towell, G.G., Shavlik, J.W.: Knowledge-based artificial neural networks. Artificial Intelligence 70, 119–165 (1994)
Happel, B.M., Murre, J.J.: Design and Evolution of Modular Neural Network Architectures. Neural Networks 7, 985–1004 (1994)
Hansen, L., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Banerjee, M., Mitra, S., Pal, S.K.: Rough fuzzy MLP: Knowledge encoding and classification. IEEE Transactions on Neural Networks 9(6), 1203–1216 (1998)
Pal, S.K., Mitra, S.: Multi-layer perceptron, fuzzy sets and classification. IEEE Transactions on Neural Networks 3, 683–697 (1992)
Maniezzo, V.: Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks 5, 39–53 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mitra, P., Mitra, S., Pal, S.K. (1999). Modular Rough Fuzzy MLP: Evolutionary Design. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-48061-7_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
Online ISBN: 978-3-540-48061-7
eBook Packages: Springer Book Archive