Genetic tuning of fuzzy inference within fuzzy classifier systems

被引:19
作者
Crockett, KA [1 ]
Bandar, Z [1 ]
Fowdar, J [1 ]
O'Shea, J [1 ]
机构
[1] Manchester Metropolitan Univ, Dept Comp & Math, Intelligent Syst Grp, Manchester M15 9GD, Lancs, England
关键词
fuzzy inference systems; genetic algorithms; membership functions;
D O I
10.1111/j.1468-0394.2006.00325.x
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
In generating a suitable fuzzy classifier system, significant effort is often placed on the determination and the fine tuning of the fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within the fuzzy rules. Often traditional fuzzy inference strategies are used which consequently provide no control over how strongly or weakly the inference is applied within these rules. Furthermore such strategies will allow no interaction between grades of membership. A number of theoretical fuzzy inference operators have been proposed for both regression and classification problems but they have not been investigated in the context of real-world applications. In this paper we propose a novel genetic algorithm framework for optimizing the strength of fuzzy inference operators concurrently with the tuning of membership functions for a given fuzzy classifier system. Each fuzzy system is generated using two well-established decision tree algorithms: C4.5 and CHAID. This will enable both classification and regression problems to be addressed within the framework. Each solution generated by the genetic algorithm will produce a set of fuzzy membership functions and also determine how strongly the inference will be applied within each fuzzy rule. We investigate several theoretical proven fuzzy inference techniques (T-norms) in the context of both classification and regression problems. The methodology proposed is applied to a number of real-world data sets in order to determine the effects of the simultaneous tuning of membership functions and inference parameters on the accuracy and robustness of fuzzy classifiers.
引用
收藏
页码:63 / 82
页数:20
相关论文
共 59 条
[1]
[Anonymous], COMPUTATIONAL INTELL
[2]
[Anonymous], 1975, Ann Arbor
[3]
[Anonymous], 1994, The Fuzzy Systems Handbook
[4]
*ATT SOFTW LTD, 2005, XPERT RUL PROF SOFTW
[5]
Batyrshin I., 1998, 6th European Congress on Intelligent Techniques and Soft Computing. EUFIT '98, P52
[6]
Blake C.L., 1998, UCI repository of machine learning databases
[7]
BONISSONE PP, 2002, ADAPTIVE NEURAL FUZZ
[8]
BRAND E, 1998, DATA MINING SOLUTI S
[9]
Breiman L., 1998, CLASSIFICATION REGRE
[10]
Carmona P, 2005, IEEE INT CONF FUZZY, P702