.png){kind=icon}
返回
Handling continuous attributes in an evolutionary inductive learner
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2004.837752.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper analyzes experimentally discretization algorithms for handling continuous attributes in evolutionary learning. We consider a learning system that induces a set of rules in a fragment of first-order logic (evolutionary inductive logic programming), and introduce a method where a given discretization algorithm is used to generate initial inequalities, which describe subranges of attributes' values. Mutation operators exploiting information on the class label of the examples (supervised discretization) are used during the learning process for refining inequalities. The evolutionary learning system is used as a platform for testing experimentally four algorithms: two variants of the proposed method, a popular supervised discretization algorithm applied prior to induction, and a discretization method which does not use information on the class labels of the examples (unsupervised discretization). Results of experiments conducted on artificial and real life datasets suggest that the proposed method provides an effective and robust technique for handling continuous attributes by means of inequalities.
Keyword:
discretization
evolutionary computation
inductive concept learning (ICL)
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
暂无机构信息
引用论文
分享收藏A test of the complementary gene model for the control of biparental plastid inheritance in zonal pelargoniums.png)
Heredity
OAAI
HeredityIF0
OAAI分享收藏Electrical Conductivity of Reproductive Tissue for Detection of Estrus in Dairy Cows用于检测奶牛发情的繁殖组织电导率OAAI
OAAI分享收藏
分享收藏
查看更多内容