Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution

Alhroob, Essam and Ngahzaifa, Ab. Ghani (2018) Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution. In: 8th IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2018) , 23-25 November 2018 , Penang, Malaysia. pp. 131-135.. ISBN 978-1-5386-6324-0

Green sonochemical synthesis of few1.pdf

Download (70kB) | Preview


The fuzzy min-max (FMM) neural network is one of the most powerful neural networks that combines neural network and fuzzy set theory into a common framework for tackling pattern classification problems. FMM neural network carries out learning processes that consist of hyperbox expansion, hyperbox overlap test and hyperbox contraction to execute pattern classification. Although these processes provide FMM with several outstanding features and make it a unique pattern classifier, the contraction process is considered a major limitation that affects the FMM learning process and hinders it from handling hyperbox overlapped boundaries appropriately. This drawback could affect membership decision making and cause the classifier to provide random decisions when test samples have the same fitness values with different hyperboxes from different classes (ambiguity issue). The performance of the classifier consequently declines. Thus, this study aims to provide a conceptual solution called `fuzzy min-max classifier based on new membership function' through a new method, `Euclidean distance', in the test phase to handle the hyperbox overlapping boundaries of different classes. The conceptual solution has not been implemented and tested in a real scenario. Hence, the application of the conceptual solution to real scenarios is recommended in future studies to assess its performance.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Pattern classification; Hyperbox contraction; Membership function
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 18 Jun 2019 04:05
Last Modified: 18 Jun 2019 04:05
URI: http://umpir.ump.edu.my/id/eprint/25103
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item