Abstract
This paper presents a parallel and distributed approach to ensemble learning of Fuzzy ARTMAP classifiers based on the multi-agent platform. Neural networks have been used successfully in a broad range of non-linear problems that are difficult to solve using traditional techniques. Training a neural network for practical applications is often time consuming thus extensive research work is being carried out to accelerate this process. Fuzzy ARTMAP (FAM) is one of the fastest neural network architectures given its ability to produce neurons on demand to represent new classification categories. FAM can adapt to the input data without having to specify an arbitrary structure. However, FAM is vulnerable to noisy data which can rapidly degrade network performance. Due to its fast learning features, FAM is sensitive to the sequence of input sample presentations. In this paper we propose a parallel and distributed approach to ensemble learning for FAM networks as a means to improve the over-all performance of the classifier and increase its resilience to noisy data. We use the multi-agent platform to distribute the computational load of the ensemble to several hosts. The multi-agent platform is a robust environment that can support large-scale neural network ensembles. Our approach also demonstrates the feasibility of large-scale ensembles. The experimental results show that ensemble learning substantially improved the performance of fuzzy ARTMAP classifiers.
This research was supported by the Program for the Training of Graduate Students in Regional Innovation which was conducted by the Ministry of Commerce Industry and Energy of the Korean Government.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Valentini, G., Masulli, F.: Ensembles of learning machines. In: Marinaro, M., Tagliaferri, R. (eds.) WIRN 2002. LNCS, vol. 2486, p. 3. Springer, Heidelberg (2002)
Maqsood, I., et al.: An Ensemble of Neural Networks for weather forcasting. Neural Computing and its Applications 13, 112–122 (2004)
Hansen, L., et al.: Neural Network Ensembles. IEEE Transactions on Pattern Analysis 12(10), 993–1001 (1990)
Carpenter, A., et al.: Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps. IEEE Transactions on Neural Networks 3(5), 698–713 (1992)
Castro, J., et al.: Parallelization of Fuzzy ARTMAP to improve its convergence speed: The network partitioning approach and the data partitioning approach. Non-Linear Analysis (2005), Available online at http://www.sciencedirect.com
Andonie, A., et al.: A Modified Fuzzy ARTMAP Architecture for Incremental Learning Function Approximation. In: Proceedings of Neural Networks and Computational Intelligence (2003)
Carpenter, A., et al.: Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System. In: Proceedings of the Seventh International Conference on Information Fusion (2004)
Parrado-Hernández, E., et al.: Study of Distributed Learning as a Solution to Category Proliferation in Fuzzy ARTMAP based Neural Systems. Neural Networks 16, 1039–1057 (2003)
Hartono, P., et al.: Learning from Imperfect Data. Applied Soft Computing (2006), Available online at http://www.sciencedirect.com
Calisti, M.: Abstracting Communication in Distributed Agent-Based Systems. In: ECOOP2002, Workshop on Concrete Communication Abstractions of The Next 701 Distributed Object Systems (2002)
Lim, C., et al.: Applications of Fuzzy ARTMAP and Fuzzy C-Means Clustering to Pattern Classification with Incomplete Data. Neural Computing and Applications 14, 104–113 (2005)
Zeng, X., et al.: Using Neural Network to Approximate an Ensemble of Classifiers. Neural Processing Letters 12, 225–237 (2000)
Downs, J., et al.: Application of the Fuzzy ARTMAP Neural Network Model to Medical Pattern Classification Tasks. Artificial Intelligence in Medicine 8, 403–428 (1996)
Blake, C., et al.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, California, Available at http://www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cervantes, L., Lee, Js., Lee, J. (2007). Agent-Based Approach to Distributed Ensemble Learning of Fuzzy ARTMAP Classifiers. In: Nguyen, N.T., Grzech, A., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2007. Lecture Notes in Computer Science(), vol 4496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72830-6_84
Download citation
DOI: https://doi.org/10.1007/978-3-540-72830-6_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72829-0
Online ISBN: 978-3-540-72830-6
eBook Packages: Computer ScienceComputer Science (R0)