Investigation of contraction process issue in fuzzy min-max models
Artikel i vetenskaplig tidskrift, 2022

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.

contraction process

pattern classification

fuzzy min-max

FMM models


Essam Alhroob

Al Zaytoonah Univ Jordan

Mohammed Falah Mohammed

Univ Zakho

Fadhl Mohammad Omar Hujainah

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering, Cyber Physical Systems

Osama Nayel Al Sayaydeh

Universiti Malaysia Pahang

Ngahzaifa Ab Ghani

Universiti Malaysia Pahang

International Journal of Data Mining, Modelling and Management

1759-1163 (ISSN) 1759-1171 (eISSN)

Vol. 14 1 1-14


Datavetenskap (datalogi)



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