计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 161-167.doi: 10.11896/jsjkx.210500211
王子茵1,3, 李磊军2,3,4, 米据生2,3, 李美争1, 解滨1
WANG Zi-yin1,3, LI Lei-jun2,3,4, MI Ju-sheng2,3, LI Mei-zheng1, XIE Bin1
摘要: 属性约简目前是粗糙集领域的热点研究问题。文中研究了如何在保持误分类代价不增加的基础上减少冗余属性。首先定义了变精度模糊粗糙集中的最小误分类程度,然后引入了决策过程,提出了一种基于最小误分类程度的变精度模糊粗糙集模型,最后在这个模型的基础上将误分代价作为不变量,提出了一种启发式属性约简算法。将所提算法与其他算法进行比较,实验结果表明,所提算法得到的属性约简结果具有保留的属性数相对较少、误分类代价更低的优点。
中图分类号:
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