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中文摘要：
为获取连续属性数据集的最小属性子集,提出一种基于模糊粗糙集和人工蜂群算法的约简方法。首先由边缘蕴含算子和t-模给出集合的模糊粗糙近似,以下近似构建模糊粗糙正域,并据此确定决策属性对条件属性集的依赖度,然后通过依赖度和约简率构建能够反映属性集大小和重要性的目标函数,将属性约简问题转化为优化问题,最后以目标函数为迭代准则,利用人工蜂群优化算法完成数据集的属性约简。仿真结果表明：该方法在不降低分类正确率的同时,可以有效降低属性维数。
英文摘要：
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.
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