Genetic-based tuning of fuzzy Dempster-Shafer model

被引：0

作者：

Sosnowski, ZA ^{[1
]}

Walijewski, JS ^{[1
]}

机构：

[1] Bialystok Tech Univ, Dept Comp Sci, PL-15351 Bialystok, Poland

来源：

COMPUTATIONAL INTELLIGENCE, THEORY AND APPLICATIONS | 2005年

关键词：

D O I：

暂无

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 study, we discuss the use of Dempster-Shafer theory as a well-rounded algorithmic vehicle in the construction of fuzzy decision rules. The concept of fuzzy granulation realized via fuzzy clustering is aimed at the discretization of continuous attributes. Next we use Genetic Algorithms (GA) to find the best points of division for discretization of continuous attributes. The rules, generated using Fuzzy Dempster-Shafer model (FDS), were verified by GA methods. The natural crossover improved by random changes (mutation and selection) can help us to find the best set of rules.

引用

页码：747 / 755

页数：9

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