基于多目标邻域差分进化和模糊粗糙集的属性约简算法

Attribute reduction with fuzzy rough set based on multiobjective neighborhood difference algorithm

作为粗糙集的一种推广,模糊粗糙集在属性约简中的应用尤为重要.约简规模和约简依赖度作为评判约简性能的两个重要指标,分别对应着约简的效率以及精度.传统的约简算法通常以追求约简的最大依赖度为导向进行寻优,并没有直接考虑约简的规模大小.基于此,强调所得约简的规模大小在约简运算中的重要性,并提出一种基于邻域变异信息的多目标差分算法,在约简运算中将约简的规模也作为单独的优化目标,将属性约简问题转化为多目标优化问题,综合考虑约简在属性数量和依赖度两方面的性能.通过引入目标支配排序,使得可以从属性数量和依赖度误差两方面对所得约简的性能进行约束,并得到目标约束内的约简结果.选取UCI上的数据集进行实验分析,实验结果表明,所提算法可以在目标约束内得到更加全面的约简结果,具有一定的可行性,是一种有效的约简算法.

As a generalization of rough set,fuzzy rough set plays a significant role in attribute reduction.Reduct size and dependency degree are two important evaluation criteria to measure reduction performance.Traditional reduction algorithms are mainly designed with the direction of maximizing dependency degree and reduct size is not taken into consideration.This paper emphasizes the significance of reduct size and proposes an multiobjective difference algorithm based on neighborhood mutation information,which makes reduct size a separate objective in attribute reduction.Hence the reduction problem is turned into multiobjective optimization problem,which considers reduction performance from aspects of both attribute number and dependency degree.By using goal-sequence domination scheme,the reduction performance can be stipulated from aspects of attribute number and dependency degree and satisfactory reduction results can be obtained.Several UCI data sets are applied to analyze the algorithm performance.The experimental results show that this method can obtain more comprehensive reduction results,which is feasible and effective.