Fuzzy rule extraction using recombined RecBF for very-imbalanced datasets

被引：0
|
作者：
Soler, V [1 ]
Roig, J [1 ]
Prim, M [1 ]
机构：
[1] Dept Microelect & Elect Syst, Bellaterra 08193, Spain
来源：
ARTIFICIAL NEURAL NETWORKS: FORMAL MODELS AND THEIR APPLICATIONS - ICANN 2005, PT 2, PROCEEDINGS | 2005年 / 3697卷
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：
An introduction to how to use RecBF to work with very-imbalanced datasets is described. In this paper, given a very-imbalanced dataset obtained from medicine, a set of Membership Functions (MF) and Fuzzy Rules are extracted. The core of this method is a recombination of the Membership Functions given by the RecBF algorithm which provides a better generalization than the original one. The results thus obtained can be interpreted as sets of low number of rules and MF.
引用
收藏
页码：685 / 690
页数：6
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