Dual Vigilance Fuzzy Adaptive Resonance Theory
Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to their internal categorical representation. This study extends the capabilities of ART by incorporating multiple vigilance thresholds in a single network: stricter (data compression) and looser (cluster similarity) vigilance values are used to obtain a many-to-one mapping of categories-to-clusters. It demonstrates this idea in the context of Fuzzy ART, presented as Dual Vigilance Fuzzy ART (DVFA), to improve the ability to capture clusters with arbitrary geometry. DVFA outperformed Fuzzy ART for the datasets in our experiments while yielding a statistically-comparable performance to another more complex, multi-prototype Fuzzy ART-based architecture.
L. E. Brito da Silva et al., "Dual Vigilance Fuzzy Adaptive Resonance Theory," Neural Networks, vol. 109, Elsevier Ltd, Jan 2019.
The definitive version is available at https://doi.org/10.1016/j.neunet.2018.09.015
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Adaptive resonance theory; ART; Clustering; Topology; Unsupervised; Visual assessment of cluster tendency
International Standard Serial Number (ISSN)
Article - Journal
© 2019 Elsevier Ltd, All rights reserved.
01 Jan 2019