Hybrid learning models to get the interpretability-accuracy trade-off in fuzzy modeling

被引:72
作者
Alcalá, R [1 ]
Alcalá-Fdez, J [1 ]
Casillas, J [1 ]
Cordón, O [1 ]
Herrera, F [1 ]
机构
[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain
关键词
linguistic fuzzy modeling; interpretability-accuracy trade-off; rule selection; weighted linguistic rules; tuning of membership functions; genetic algorithms;
D O I
10.1007/s00500-005-0002-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when modeling some complex systems. To overcome this problem, many different possibilities of improving the accuracy of linguistic fuzzy modeling have been considered in the specialized literature. We will call these approaches as basic refinement approaches. In this work, we present a short study of how these basic approaches can be combined to obtain new hybrid approaches presenting a better trade-off between interpretability and accuracy. As an example of application of these kinds of systems, we analyze seven hybrid approaches to develop accurate and still interpretable fuzzy rule-based systems, which will be tested considering two real-world problems.
引用
收藏
页码:717 / 734
页数:18
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