Dual Vigilance Hypersphere Adaptive Resonance Theory
The internal representation of the categories in Adaptive Resonance Theory (ART) neural networks can greatly affect the quality and compactness of the discovered clusters. Dual vigilance thresholds have been shown to yield a significant improvement in Fuzzy ART performance and allow it to retrieve arbitrarily shaped clusters while maintaining the important advantage of a simple architecture and implementation. In this study, we examine the use of Hypersphere ART within the same dual vigilance architecture, thereby presenting the Dual Vigilance Hypersphere ART (DVHA). We conduct an extensive comparison between 6 different ART-based approaches across a set of 30 benchmark datasets, using two different input ordering methods and 30 repeated runs. We find that DVHA ranks better than Dual Vigilance Fuzzy ART (DVFA) on average for many datasets, both in terms of performance and network compactness. Furthermore, although another multi-category-based architecture showed statistically superior results when the inputs are shuffled, we found no statistical difference in performance when the input was pre-processed using the visual assessment of cluster tendency (VAT), while generally being much simpler to implement and less computationally demanding. These findings make DVHA a viable alternative to its Fuzzy ART counterpart, and a simpler alternative to the other studied multi-category-based approaches in cases where resources are limited, such as embedded and hardware-based applications, provided that the input can be preprocessed using VAT.
I. Elnabarawy et al., "Dual Vigilance Hypersphere Adaptive Resonance Theory," Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (2019, Xiamen, China), pp. 2425 - 2432, Institute of Electrical and Electronics Engineers (IEEE), Dec 2019.
The definitive version is available at https://doi.org/10.1109/SSCI44817.2019.9002721
2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (2019: Dec. 6-9, Xiamen, China)
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Adaptive Resonance Theory; Clustering; Hypersphere; Topology; Unsupervised; Visual Assessment of Cluster Tendency
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Dec 2019