Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/inderscience-publishers/investigation-of-contraction-process-issue-in-fuzzy-min-max-models-fjJQMRpIYT
References
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
- Publisher
- Inderscience Publishers
- Copyright
- Copyright © Inderscience Enterprises Ltd
- ISSN
- 1759-1163
- eISSN
- 1759-1171
- DOI
- 10.1504/ijdmmm.2022.122034
- Publisher site
- See Article on Publisher Site
Abstract
The fuzzy min-max (FMM) network is one of the most powerful neural networks. It combines a neural network and fuzzy sets into a unified framework to address pattern classification problems. The FMM consists of three main learning processes, namely, hyperbox contraction, hyperbox expansion and hyperbox overlap tests. Despite its various learning processes, the contraction process is considered as one of the major challenges in the FMM that affects the classification process. Thus, this study aims to investigate the FMM contraction process precisely to highlight its usage consequences during the learning process. Such investigation can assist practitioners and researchers in obtaining a better understanding about the consequences of using the contraction process on the network performance. Findings of this study indicate that the contraction process used in FMM can affect network performance in terms of misclassification and incapability in handling the membership ambiguity of the overlapping regions.
Journal
International Journal of Data Mining – Inderscience Publishers
Published: Jan 1, 2022