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

被引：0
|
作者：
Tormasi, A. [1 ]
Koczy, L. T. [1 ,2 ]
机构：
[1] Szechenyi Istvan Univ, Dept Automat, Gyor, Hungary
[2] Budapest Univ Technol & Econ, Dept Telecommun & Mediainformat, Budapest, Hungary
来源：
RECENT DEVELOPMENTS AND NEW DIRECTION IN SOFT-COMPUTING FOUNDATIONS AND APPLICATIONS | 2016年 / 342卷
基金：
匈牙利科学研究基金会;
关键词：
D O I：
10.1007/978-3-319-32229-2_12
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：157 / 169
页数：13
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