中南大学学报（自然科学版）2013，Vol.44Issue(1)：172-178,7.
基于模糊粗糙集和蜂群算法的属性约简
Attribute reduction method based on fuzzy rough sets and artificial bee colony algorithm
摘要
Abstract
To acquire the minimum attribute reduction of the dataset with continual attribute values, a novel method was proposed on the basis of fuzzy rough sets (FRS) and artificial bee colony (ABC) algorithm. Firstly, the fuzzy rough approximation of the sets was given via a border implicator and a t-norm, and the fuzzy rough positive region and dependency of the decision attribute about the condition attribute have generated using this approximation. Then, an objective function indicating the importance and size of attribute sets was constructed based on the concept of dependency and reduction rate. Via this operation, the problem of attribute reduction was converted to that of optimization. Finally, the attribute reduction for datasets was performed using the ABC algorithm under the guidance of the objective function value. Experimental results show that the strategy can reduce the attribute dimensions efficiently without sacrificing the classification accuracy.关键词
属性约简/粗糙集理论/模糊粗糙集/依赖性/人工蜂群算法Key words
attribute reduction/ rough set theory/ fuzzy rough sets/ dependency/ artificial bee colony algorithm分类
信息技术与安全科学引用本文复制引用
王世强,张登福,毕笃彦,张立东,王占领,李洋,雍霄驹..基于模糊粗糙集和蜂群算法的属性约简[J].中南大学学报（自然科学版）,2013,44(1):172-178,7.基金项目
国家自然科学基金资助项目(61175029) （61175029）
国防科技重点实验室基金资助项目(9140C610301080C6106,9140C6001070801):航空科学基金资助项目(20095596014,20101996009) （9140C610301080C6106,9140C6001070801）
陕西省自然科学基金资助项目(2009JM8001-4) （2009JM8001-4）
