基于距离比值尺度的模糊粗糙集属性约简

属性约简能有效地去除不必要属性,提高分类器的性能。模糊粗糙集是处理不确定信息的重要范式,能有效地应用于属性约简。在模糊粗糙集中,样本分布的不确定性会影响对象的近似集,进而影响有效属性约简的获取。为有效地定义近似集,文中提出了基于距离比值尺度的模糊粗糙集,该模型引入了基于距离比值尺度的样本集的定义,通过对距离比值尺度的控制,避免了样本分布不确定性对近似集的影响;给出了该模型的基本性质,定义了新的依赖度函数,进而设计了属性约简算法;以SVM,NaiveBayes和J48作为测试分类器,在UCI数据集上评测所提算法的性能。实验结果表明,所提出的属性约简算法能够有效获取约简并提高分类的精度。

Attribute Reduction of Fuzzy Rough Set Based on Distance Ratio Scale

Attribute reduction can effectively remove the unnecessary attributes in order to improve the performance of the classifiers.Fuzzy rough set theory is an important formal of processing the uncertain information.In the fuzzy rough set model,the approximations of an object may be affected by uncertain distribution of samples.Consequently,acquiring effective attribute reduction may be influenced.In order to effectively define approximations,this paper proposed a novel fuzzy rough set model named distance ratio scale based fuzzy rough set.The definition of samples based on distance ratio scale is introduced.The influence of uncertain distribution of samples to approximations is avoided by controlling the distance ratio scale.The basic properties of this fuzzy rough set model are presented and the new dependent function is defined.Furthermore,the algorithm for attribute reduction is designed.SVM,NaiveBayes,and J48 were used as test classifier executed on UCI data sets to verify the performance of the proposed algorithm.The experimental results show that attribute reduction can be effectively obtained by the proposed attribute reduction algorithm and the classification precisions of classifiers are improved.