A Study on Exploiting VAT to Mitigate Ordering Effects in Fuzzy ART
The clustering structures formed by Adaptive Resonance Theory (ART) and many other algorithms are dependent on input presentation/permutation order. In this work, we exploit Visual Assessment of cluster Tendency (VAT) as a pre-processor for Fuzzy ART in order to mitigate this problem. This approach is a global strategy that uses similarity-based ordering before clustering. Experimental results show that this framework improved peak and average performance, reduced the number of categories, and incurred less variability in the clustering outcome. By enhancing performance and reducing sensitivity to input order presentation, this approach is recommended when it is suitable to perform off-line incremental learning.
L. E. da Silva and D. C. Wunsch, "A Study on Exploiting VAT to Mitigate Ordering Effects in Fuzzy ART," Proceedings of the International Joint Conference on Neural Networks (2018, Rio de Janeiro, Brazil), Institute of Electrical and Electronics Engineers (IEEE), Jul 2018.
The definitive version is available at https://doi.org/10.1109/IJCNN.2018.8489724
2018 International Joint Conference on Neural Networks, IJCNN 2018 (2018: Jul. 8-13, Rio de Janeiro, Brazil)
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Adaptive Resonance Theory; Fuzzy ART; Global Strategies; Incremental Learning; Ordering Effects; Visual Assessment Of Cluster Tendency, Neural Networks
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jul 2018