最大泛化规则生成

• 作者: 徐如燕 郭齐胜 鲁汉榕
• 来源期刊: 计算机科学
• 学科: 工学
• 关键词: Classification knowledge discovery,Maximally generalized rules,Information theory,J-measure Significance
• 年，卷（期）: 2001,（8）
• 所属期刊栏目: 人工智能及其他
• 页码范围: 114-115,113
• 页数: 3页
• 分类号: TP39
• 字数: 2477字
• 语种: 中文
• DOI: 10.3969/j.issn.1002-137X.2001.08.027

摘要

In this paper,the generation of maximally generalized rules in the course of classitication knowledge discovery based on rough sets theory is discussed. Firstly, an algorithm is introduced. Secondly,we propose that the information-based J-measure is used as another measure of attribute signifi cance value. This measure is used for heuristically selecting the conditions to be removed in the process of extracting a set of maximally generalized rules. Finally,we present an example to illustrate the process of the algorithm.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	郭齐胜		187	1167	17.0	24.0
2	鲁汉榕		47	262	10.0	14.0
3	徐如燕		8	71	6.0	8.0