ALGORITHM TO LEARN CLASIFICATION RULES BASED ON THE EXTENDED ROUGH SET THEORY

被引：0
|
作者：
Filiberto, Yaima [1 ]
Bello, Rafael [2 ]
Caballero, Yaile
Frias, Mabel [1 ]
机构：
[1] Univ Camaguey, Fac Informat, Camaguey, Cuba
[2] Univ Cent Las Villas, Las Villas, Cuba
来源：
DYNA-COLOMBIA | 2011年 / 78卷 / 169期
关键词：
Classification rules; similarity relations; Rough set theory;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Rough sets have allowed developing several machine learning techniques, among them methods to discover rules of classification. In this paper, we present an algorithm to generate rules of classification based on similarity relations, this allows to apply this method in the case of features with discrete or real domains. The experimental results show a satisfactory performance of this algorithm in comparison with other such as C4.5 and MODLEM.
引用
收藏
页码：62 / 70
页数：9
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