| 一般二元关系下的近似属性约简算法 |
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| 引用本文： | 滕书华,廖帆,鲁敏,赵键,张军.一般二元关系下的近似属性约简算法[J].软件学报,2014,25(S2):169-177. |
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| 作者姓名： | 滕书华 廖帆 鲁敏 赵键 张军 |
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| 作者单位： | 国防科学技术大学 自动目标识别重点实验室, 湖南 长沙 410073,66295部队, 河北 保定 072750,国防科学技术大学 自动目标识别重点实验室, 湖南 长沙 410073,国防科学技术大学 自动目标识别重点实验室, 湖南 长沙 410073,国防科学技术大学 自动目标识别重点实验室, 湖南 长沙 410073 |
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| 基金项目： | 国家自然科学基金(61471371);湖南省自然科学基金(2015jj3022);中国博士后科学基金(2012M512168) |
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| 摘 要： | 属性约简是粗糙集理论重要应用之一.考虑到决策信息系统中的噪声,针对一般二元关系,从知识分类能力角度给出了一种新的属性重要性度量方法,在此基础上提出了一种能够抑制噪声的近似属性约简算法,该算法适用于多种粗糙集扩展模型,摆脱了现有约简算法对特定二元关系的依赖.实验结果表明,近似约简算法通过调节近似参数,可有效增强抗噪性,在有效降低约简属性集规模的同时,提高了约简结果的分类性能.
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| 关 键 词： | 噪声 粗糙集 属性约简 一般二元关系 |
| 收稿时间： | 5/7/2014 12:00:00 AM |
| 修稿时间： | 2014/8/19 0:00:00 |
Approximate Attribute Reduction Algorithm Based on General Binary Relation |
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| TENG Shu-Hu,LIAO Fan,LU Min,ZHAO Jian and ZHANG Jun.Approximate Attribute Reduction Algorithm Based on General Binary Relation[J].Journal of Software,2014,25(S2):169-177. |
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| Authors: | TENG Shu-Hu LIAO Fan LU Min ZHAO Jian and ZHANG Jun |
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| Affiliation: | Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China,PLA Units: 66295, Baoding 072750, China,Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China,Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China and Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China |
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| Abstract: | One of the most attentive applications of rough set is attribute reduction. Addressing the noise in decision information systems, a new method for importance measure of attribute set is presented from the point of view that knowledge can enhance the ability to perform classification. In addition, a new approximate attribute reduction algorithms is proposed based on general binary relation, which can be used to deal with noise and be applicable to many extending model of rough sets. Experimental results demonstrate that the proposed approximate attribute reduction algorithms can effectively increase sensitivity to noise, achieve more compact reduction, and simultaneously improve the classification performance. |
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| Keywords: | noise rough set attribute reduction general binary relation |
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