基于邻域粗糙集的符号与数值属性快速约简算法<sup>*</sup>

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摘要粗糙集理论被广泛应用于属性约简,算法复杂性是制约约简应用于大样本知识发现的主要问题,尤其是邻域模型下的约简问题.本文分析邻域粗糙集模型的数学性质,利用正域与属性集的单调关系,构造基于属性依赖度和前向搜索策略的快速算法.该算法降低样本比较次数,提高计算效率.实验分析表明该算法的有效性.

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	胡清华
	赵辉
	于达仁

关键词 ：粗糙集, 属性约简, 邻域, 属性重要度, 快速算法

Abstract：Rough set theory is widely used in attribute reduction. Computational complexity is one of the factors to limit applicability in reduction techniques, especially in the neighborhood rough set based reduction. In this paper, some mathematical properties of neighborhood rough set model are analyzed. An efficient method is proposed for forward attribute selection strategy based on dependency by using the property that positive region monotonously increases with the amount of attributes. By this algorithm, the comparison times of the samples in computing positive region and neighborhood are reduced, and thus the computational efficiency is improved. The experimental results show that the proposed method is effective.

Key words： Rough Set Attribute Reduction Neighborhood Attribute Significance Efficient Algorithm

收稿日期: 2007-06-01

ZTFLH:

TP181

基金资助:国家自然科学基金资助项目(No.60703013)

作者简介: 胡清华,男,1976年生,博士研究生,主要研究方向为混合数据粗糙计算、属性约简和多分类器学习.E-mail:huqinghua@hcms.hit.edu.cn.赵辉,男,1983年生,博士研究生,主要研究方向为粗糙集理论与应用.于达仁,男,1966年生,教授,博士生导师,主要研究方向为先进动力系统的智能控制.

引用本文:

胡清华，赵辉，于达仁. 基于邻域粗糙集的符号与数值属性快速约简算法^*[J]. 模式识别与人工智能, 2008, 21(6): 730-738. HU Qing-Hua, ZHAO Hui, YU Da-Ren. Efficient Symbolic and Numerical Attribute Reduction with Neighborhood Rough Sets. , 2008, 21(6): 730-738.

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http://manu46.magtech.com.cn/Jweb_prai/CN/ 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2008/V21/I6/730

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