摘要
提出一种基于分类目标的启发式离散化算法,通过该算法能够解决粗糙集理论中的连续属性离散化问题.该算法充分考虑目标分类和属性的重要性,在减少决策规则的同时完成了属性约简.通过茶味觉信号的验证及与传统算法结果的比较,验证了所给算法的有效性.
In order to solve the problem of rough sets theory in continuous attributes discretization a heuristic discretization algorighm on objective planning is proposed. With the wealthy calculation on objective planning and the importance of attributes considered, the heuristic discretization algorithm not only reduces decision rulers but also finishes attribute value reduction. The comparison between the algorithms in this paper and the other paper on teataste signals shows the algorithm in this paper produces less rulers and the short average length of rulers. The rulers in this algorithm have been decided, too. Consequently, the decision system has ahigh efficiency.
出处
《吉林大学学报(理学版)》
CAS
CSCD
北大核心
2009年第6期1251-1254,共4页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:6067309960873146)
国家高技术研究发展计划863项目基金(批准号:2007AA04Z1142009AA02Z307)
吉林省生物识别新技术重点实验室项目基金(批准号:20082209)
吉林省教育厅科技发展计划项目基金(批准号:吉教科合字[2007]第172号)
关键词
粗糙集
重要度
离散化
区间划分
决策规则
rough set
importance degree
discretization
interval partition
decision ruler