Fuzzy Classification with Multi-objective Evolutionary Algorithms

被引：0
|
作者：
Jimenez, Fernando [1 ]
Sanchez, Gracia [1 ]
Sanchez, Jose F. [1 ]
Alcaraz, Jose M. [1 ]
机构：
[1] Fac Informat Campus Espinardo, Murcia 30071, Spain
来源：
HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS | 2008年 / 5271卷
关键词：
Multi-objective Evolutionary algorithms; Fuzzy classification;
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 work we propose, on the one hand, a multi-objective constrained optimization model to obtain fuzzy models for classification considering criteria of accuracy and interpretability. On the other hand, we propose an evolutionary multi-objective approach for fuzzy classification from data with real and discrete attributes. The multi-objective evolutionary approach has been evaluated by means of three different evolutionary schemes: Preselection with niches, NSGA-II and ENORA. The results have been compared in terms of effectiveness by means of statistical techniques using the well-known standard Iris data set.
引用
收藏
页码：730 / 738
页数：9
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