Fuzzy classification using probability-based rule weighting

被引：0
|
作者：
van den Berg, J [1 ]
Kaymak, U [1 ]
van den Bergh, WM [1 ]
机构：
[1] Erasmus Univ, Fac Econ, NL-3000 DR Rotterdam, Netherlands
来源：
PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOL 1 & 2 | 2002年
关键词：
fuzzy classification; certainty factors; rule weighting; probabilistic fuzzy models;
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：
Design of fuzzy classifiers based on probabilistic fuzzy systems is considered. It is shown that the statistical properties of the training data can be used for the design of fuzzy rule based classification systems. Takagi-Sugeno type fuzzy systems are designed for estimating the underlying conditional probability density function for the data. Probabilistic rule weighting is introduced, and classifiers based on the discriminant function approach are formulated. It is shown that some of the fuzzy classifiers that have been proposed in the literature can be formulated in terms of probabilistic rule weighting. Furthermore, the relation to certainty factor approach to fuzzy classifiers is considered.
引用
收藏
页码：991 / 996
页数：6
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