Dual Vigilance Fuzzy Adaptive Resonance Theory
Abstract
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.
Recommended Citation
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
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
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)
0893-6080
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Elsevier Ltd, All rights reserved.
Publication Date
01 Jan 2019
Comments
Research was sponsored in part by the Missouri University of Science and Technology Mary K. Finley Endowment and Intelligent Systems Center, USA ; the Coordenacao de Aperfeiçoamento de Pessoal de NÃvel Superior - Brazil (CAPES) - Finance code BEX 13494/13-9 ; and the Army Research Laboratory (ARL), USA , and was accomplished under Cooperative Agreement Number W911NF-18-2-0260.