一种基于重要度的实值粗糙集模型及其在交通状态辨识中的应用  

A Real Rough Set Model Based on Significance and Its Applications to Traffic Condition Recognition

作　　者：张慧哲[1] 王坚[1] 任子晖[1] 

机构地区：[1]同济大学CIMS研究中心,上海201804

出　　处：《信息与控制》2009年第5期591-595,共5页Information and Control

摘　　要：在分析现有粗糙集理论和算法在处理实值决策系统问题的局限性的基础上,提出了一种新的实值属性重要度定义,并在此定义基础上提出了实值粗糙集扩展模型及其属性快速约简算法,避免了经典粗糙集理论必须离散化数据的弊端.最后将所提出的算法和其它算法应用于区域交通状态辨识中,对比测试结果验证了所提出的方法具有较高的分类准确率.The notion of a new real attribute significance is introduced by analyzing the limitations of the current theories and algorithms about the rough sets applied to real decision system. Then a real rough set model and the fast attribute reduction algorithm are presented based on the presented notion, which can avoid data discretization in traditional rough set theories. Finally, the presented algorithm and several other algorithms are applied to region traffic condition recognition, and the comparison results show that the proposed algorithm is of high classification accuracy.

关 键 词：粗糙集 属性重要度 属性约简 近邻 交通状态辨识 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]