加权模糊粗糙约简

基于模糊粗糙集的传统约简算法的时间代价较高,在处理大规模数据时耗时过长,且在许多实际大规模数据集上存在有限时间内无法收敛等问题。因此将权重引入属性约简的定义中,其中属性权重是属性重要度的数值指标。通过构建优化问题来求解属性权重,证明了属性依赖度即是属性权重的最优解。因此,提出了基于属性权重排序的约简算法,从而大大提升了约简的速度,使得约简算法可以应用于大规模数据集,特别是高维数据集中。

Weighted Attribute Reduction Based on Fuzzy Rough Sets

Now the existing classical reduction algorithms have high time consumption,especially on the large scale datasets.To handle this problem,this paper introduced weights into the concept of attribute reduction,where weight is the measure of attribute significance.By building optimization problem about weights,it is fond that the attribute dependency degree is just the optimal solution of the weights.As a result,this paper proposed a reduction algorithm based on ranked weights,which significantly accelerate attribute reduction.Numerical experiments demonstrate that the proposed algorithm is suitable on large scale datasets,especially on the datasets with high dimension.