First-order rules mining guided by information gain - 文章详情页

作者：

Yang, Xinwu (Yang, Xinwu.) | Liu, Chunnian (Liu, Chunnian.) | Zhong, Ning (Zhong, Ning.)

收录：

摘要：

The　key　of　using　genetic　algorithm　to　mine　first-order　rules　is　how　to　precisely　evaluate　the　quality　of　first-order　rules.　By　adopting　the　concept　of　binding　and　information　theory,　a　new　fitness　function　based　on　information　gain　is　proposed.　The　new　fitness　function　not　only　measures　the　quality　of　first-order　rules　precisely　but　also　solves　the　equivalence　class　problem,　which　exists　in　the　common　evaluation　criteria　based　on　the　number　of　examples　covered　by　rules.

关键词：

Information retrieval Information theory Problem solving Genetic algorithms

作者机构：

[ 1 ] [Yang, Xinwu]Beijing Munic. Key Lab. M./I.S.T., School of Computer Science, Beijing University of Technology, China
[ 2 ] [Liu, Chunnian]Beijing Munic. Key Lab. M./I.S.T., School of Computer Science, Beijing University of Technology, China
[ 3 ] [Zhong, Ning]Dept. of Information Eng., Maebashi Institute of Technology, Japan

通讯作者信息：

电子邮件地址：

查看成果更多字段

成果类型
所属机构

所有年份指定年份从至