计算机科学 ›› 2017, Vol. 44 ›› Issue (9): 67-69.doi: 10.11896/j.issn.1002-137X.2017.09.013
• CRSSC-CWI-CGrC 2016 • 上一篇 下一篇
杨志荣,王宇,杨习贝
YANG Zhi-rong, WANG Yu and YANG Xi-bei
摘要： 与经典粗糙集相比,传统的决策粗糙集将代价考虑在内,利用代价矩阵生成一对阈值。但决策粗糙集不具备经典粗糙集的单调性,这为粗糙集的属性约简带来了新的挑战。传统的决策粗糙集中的代价矩阵只有一个,没有考虑到代价的变化性。首先介绍了多代价决策粗糙集下的悲观决策规则和乐观决策规则的定义,利用多个代价矩阵来生成阈值,并将其用于属性约简中。在属性约简中,从单独的决策类出发而不是基于全部的决策类提出了启发式的Local属性约简方法,且从相关实验结果中可以得到,相对于基于全部的决策类的属性约简,Local属性约简在乐观条件下比在悲观条件下能获得更多的正域规则。
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