新的基于区分对象集的邻域粗糙集属性约简算法  

New attribute reduction algorithm of neighborhood rough set based on distinguished object set

作　　者：梁海龙[1] 谢珺[1] 续欣莹[1] 任密蜂[1] 

机构地区：[1]太原理工大学信息工程学院,太原030024

出　　处：《计算机应用》2015年第8期2366-2370,共5页journal of Computer Applications

基　　金：山西省自然科学基金资助项目(2014011018-2);山西省回国留学人员科研资助项目(2013-033);山西省留学回国人员科技活动择优资助项目;太原理工大学校基金青年项目(2014QN015)

摘　　要：基于正域的属性约简算法是利用"下近似"思想,仅考虑被正确区分样本数的约简算法。借鉴"上近似"的思想,利用"邻域信息粒"的概念定义了区分对象集,探讨了其基本性质,并提出了基于区分对象集的属性重要度度量及启发式属性约简算法。该约简算法既考虑信息决策表的相对正域,也考虑以核属性为启发信息逐个增加条件属性时对边界域样本的影响。通过实例分析,说明了所提算法的可行性,并且以6个UCI标准数据集为实验对象,与基于正域的属性约简算法进行对比实验。实验结果说明,采用提出的约简算法得到的约简属性集,与基于正域的属性约简算法相比,在进行分类任务时的分类精度能够保持不变或有所提高。Since the algorithm of attribute reduction based on positive region is based on the thought of lower approximation, it just considers the right distinguished samples. Using the thought of upper approximation and the concept of neighborhood information granule, the distinguished object set with its basic characteristics was designed and analyzed, then the new attribute importance measurement based on distinguished object set and heuristic attribute reduction algorithm was proposed. The proposed algorithm considered both the relative positive region of information decision table and the influence on boundary samples when growing condition attributes. The feasibility of the algorithm was discussed by instance analysis, and the comparative experiments on UCI data set with attribute reduction algorithm based on positive region were carried out. The experimental results show that the proposed attribute reduction algorithm can get better reduction, and the classification precision of sample set can remain the same or has certain improvement.

关 键 词：属性约简 属性重要度 相对正域 邻域粗糙集 分类精度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP311.13[自动化与计算机技术—控制科学与工程]