Title

Dual Vigilance Hypersphere Adaptive Resonance Theory

Abstract

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.

Meeting Name

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (2019: Dec. 6-9, Xiamen, China)

Department(s)

Electrical and Computer Engineering

Comments

This research was sponsored by the Missouri University of Science and Technology Mary K. Finley Endowment and Intelligent Systems Center; the Coordenac¸ao de Aperfeic¸oamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance code BEX 13494/13-9; the Army Research Laboratory (ARL) and the Lifelong Learning Machines program from DARPA/MTO, and it was accomplished under Cooperative Agreement Number W911NF-18-2-0260.

Keywords and Phrases

Adaptive Resonance Theory; Clustering; Hypersphere; Topology; Unsupervised; Visual Assessment of Cluster Tendency

International Standard Book Number (ISBN)

978-172812485-8

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2019

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