基于α信息熵的模糊粗糙属性约简方法  

Fuzzy-rough attribute reduction algorithm based on α information entropy

作　　者：潘瑞林[1] 李园沁 张洪亮[1] 伊长生[1] 樊杨龙[1] 杨庭圣 PAN Rui-lin LI Yuan-qin ZHANG Hong-liang YI Chang-sheng FAN Yang-long YANG Ting-sheng(School of Management Science and Engineering, Anhui University of Technology, Maanshan 243032, Chin)

机构地区：[1]安徽工业大学管理科学与工程学院,安徽马鞍山243032

出　　处：《控制与决策》2017年第2期340-348,共9页Control and Decision

基　　金：国家自然科学基金项目(71172219;71302056);安徽省科技厅软科学重大项目(1502052006)

摘　　要：基于邻域粗糙集以及模糊粗糙集等价关系下的属性约简方法,引入α信息熵,建立模糊相似关系下的α信息熵不确定性度量,提出基于α信息熵的属性重要度度量,并以此构建混合属性约简算法.利用UCI数据集与几种相关的约简方法进行比较,验证了该方法可以选择较少属性的同时保证较高的分类精确性.实际应用中,对参数α的有效调节,可获得多个约简结果,进而可根据需要选择最佳约简.Based on the attribute reduction algorithms computed with the equivalence relation in the neighborhood-rough set or fuzzy-rough set, the paper introduces the α information entropy for measuring fuzzy similarity relation, and proposes an attribute significance measure model based on it. Using the measure as heuristic information, a reduction algorithm for hybrid attribute is proposed.Comparing to other methods, experimental results show that the proposed method can select a few attributes but keep, even improve classification ability in several UCI datasets. In practice, by tuning α we can obtain multiple reduction results, and choose the optimum attribute reduction set according to the actual needs.

关 键 词：信息熵 模糊相似关系 模糊粗糙集 约简 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]