Title: Investigation of contraction process issue in fuzzy min-max models

Authors: Essam Alhroob; Mohammed Falah Mohammed; Fadhl Hujainah; Osama Nayel Al Sayaydeh; Ngahzaifa Ab Ghani

Addresses: Faculty of Science and Information Technology, Al Zaytoonah University of Jordan, Amman, Jordan ' Department of Computer Science, University of Zakho, Duhok, KRG, Iraq ' Computer Science and Engineering Department, Chalmers and University of Gothenburg, 412 96 Gothenburg, Sweden ' Faculty of Computing, Universiti Malaysia Pahang, Kuantan 26300, Malaysia ' Faculty of Computing, Universiti Malaysia Pahang, Kuantan 26300, Malaysia; IBM Centre of Excellence (Universiti Malaysia Pahang), Cybercentre, Pahang Technology Park, 26300 Kuantan Pahang, Malaysia

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.

Keywords: pattern classification; fuzzy min-max; FMM models; contraction process.

DOI: 10.1504/IJDMMM.2022.122034

International Journal of Data Mining, Modelling and Management, 2022 Vol.14 No.1, pp.1 - 14

Received: 30 Nov 2019
Accepted: 26 Jun 2020
Published online: 08 Apr 2022 *

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