高可信度最小约简属性启发策略

尹林子; 李勇刚; 阳春华; 桂卫华

doi:10.3724/SP.J.1004.2012.01751

高可信度最小约简属性启发策略

doi: 10.3724/SP.J.1004.2012.01751

1.
中南大学信息科学与工程学院长沙 410083;
2.
中南大学物理与电子学院长沙 410083

详细信息

通讯作者:
李勇刚

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- 文章访问数: 1655
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- 被引次数: 0
出版历程
- 收稿日期: 2011-12-21
- 修回日期: 2012-03-29
- 刊出日期: 2012-11-20

High Confidence Heuristic Strategy for Minimal Reduction

1.
School of Information Science and Engineering, Central South University, Changsha 410083;
2.
School of Physics and Electronic, Central South University, Changsha 410083

摘要

摘要: 为提高启发式算法计算最小约简的可信度,基于可辨识矩阵,研究了属性之间存在的吸收、排斥以及互斥等特征,分析其与最小约简的关联,提出了对应的最小约简属性启发策略, 建立了各个特征下属性启发策略的可信度计算模型. 在此基础上,按照可信度排序,形成了一种综合的高可信度最小约简属性启发策略,并给出了具体的约简算法. 理论和实验分析表明,本文策略具有可信度高且可信度可以估计等优点,能有效提升最小约简算法的性能.
- 属性吸收 /
- 属性排斥 /
- 属性互斥 /
- 最小约简 /
- 可信度
Abstract: In order to improve the confidence of minimal reducts calculated by heuristic methods, some important characters of attributes, such as absorption, repulsion, and mutex etc., are presented based on the discernibility matrix. Then the related heuristic strategies are proposed by analyzing the relation between these characters and the minimal reducts. Some confidence models of these strategies are established to order these strategies. On the basis, an integrated strategy and a related reduction algorithm are proposed to calculate a minimal redcut. Theoretic and experimental analyses show that the proposed strategies are of high confidence and effectiveness.
- Attribute absorption /
- attribute repulsion /
- attribute mutex /
- minimal reduct /
- confidence

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参考文献(1)

[1]

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