计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 242-251.doi: 10.11896/jsjkx.180901781
于天佑1,2, 张楠1,2, 岳晓冬3, 童向荣1,2, 孔贺庆1,2
YU Tian-you1,2, ZHANG Nan1,2, YUE Xiao-dong3, TONG Xiang-rong1,2, KONG He-qing1,2
摘要： 属性约简是粗糙集理论研究的重要内容之一,通过属性约简可以获取给定信息系统的最小特征子集。经典的序决策表属性约简是关于决策属性中的所有决策类的约简,但在实际应用中,由于决策者的偏好或者部分决策类数据的缺失,往往仅需要获得特定决策类的属性约简。基于这种考虑,文中回顾了序决策表的优势关系与下近似约简,定义了基于序决策表的单特定类与多特定类下近似约简,构造了相应的差别矩阵,提出了基于多特定类的序决策表下近似属性约简算法。基于多特定类的序决策表下近似约简可以较好地退化为基于单特定类的序决策表下近似约简或基于经典全决策类的序决策表下近似约简,是一种更加广泛的约简框架。实验采用了6组UCI数据集,分别在每个数据集上计算了3个单特定类和3组多特定类的约简,并将约简结果和约简效率与经典全类下近似约简、上近似约简及最大分布约简3个算法的约简结果和约简效率进行了比较。实验结果表明,在选定的特定类的数量相对全部决策类的数量较少时,约简的结果可能会更短,约简的效率也会有不同程度的提升。
中图分类号:
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