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Erschienen in:
Software Implementation Methodology of Intelligent Information Systems of Learning and Knowledge Control (IISLKC) Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Comparing the Properties of Meta-heuristic Optimization Techniques with Various Parameters on a Fuzzy Rule-Based Classifier

verfasst von : A. Tormási, L. T. Kóczy

Erschienen in: Recent Developments and New Direction in Soft-Computing Foundations and Applications

Verlag: Springer International Publishing

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Abstract

In this paper, the results of meta-heuristic optimization techniques with various parameter settings are presented. A formerly published Fuzzy-Based Recognizer (FUBAR): A fuzzy rule-based classification algorithm was used to analyze and evaluate the behavior of the used meta-heuristic optimization algorithms for rule-base optimization. Besides the reached accuracy, the execution time, the CPU load of the algorithms, and the effects of the shapes of the fuzzy membership functions in the initial rule-base are also investigated.

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Vorheriges Kapitel Optimization by Cuckoo Search of Interval Type-2 Fuzzy Logic Systems for Edge Detection
Nächstes Kapitel A Neural Network with a Learning Vector Quantization Algorithm for Multiclass Classification Using a Modular Approach
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Metadaten
Titel
Comparing the Properties of Meta-heuristic Optimization Techniques with Various Parameters on a Fuzzy Rule-Based Classifier
verfasst von
A. Tormási
L. T. Kóczy
Copyright-Jahr
2016
Buch
Recent Developments and New Direction in Soft-Computing Foundations and Applications

Print ISBN: 978-3-319-32227-8

Electronic ISBN: 978-3-319-32229-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-32229-2_12

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