计算机科学 ›› 2018, Vol. 45 ›› Issue (7): 197-201.doi: 10.11896/j.issn.1002-137X.2018.07.034
王蓉1,刘遵仁2,纪俊2
WANG Rong1,LIU Zun-ren2,JI Jun2
摘要: 作为经典Pawlak粗糙集的扩展,邻域粗糙集能有效处理数值型的数据。但是,因为引入了邻域粒化的概念,所以邻域实数空间下的计算量要比经典离散空间下的计算量大得多。对于邻域粗糙集算法而言,能够有效且快速地找到数据集的属性约简是十分有意义的。为此,针对现有算法中属性重要度定义的不足,首先提出了一种改进的投票式属性重要度,然后进一步提出了一种基于投票式属性重要度的快速属性约简算法。实验证明,与现有算法相比,在保证分类精度的前提下,该算法能更快速地得到属性约简。
中图分类号:
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