Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72830-6_84 | KES-AMSTA |
Keywords | Field | DocType |
ensemble learning,neural network,network performance | Data mining,Noisy data,On demand,Computer science,Fuzzy logic,Artificial intelligence,Classifier (linguistics),Artificial neural network,Ensemble learning,Machine learning,Network performance | Conference |
Volume | ISSN | Citations |
4496 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Louie F. Cervantes | 1 | 18 | 2.43 |
| J Lee | 2 | 30 | 5.10 |
| Jaewan Lee | 3 | 62 | 14.66 |