摘要
无论基于分类质量还是基于相对正域的变精度粗糙集区间约简模型都存在多种异常,根本原因是约简过程中条件类的粒度发生变化,且分类质量、相对正域和下近似分布三者不再等价变化.为消除现有约简模型存在的约简异常,文中基于下近似分布不变重新定义了区间约简模型,并给出一种基于有序分辨矩阵的区间约简方法.最后将3种区间约简模型分别应用于Wine数据集,演示不同约简模型结果间的联系与区别.
Interval reduction models based on classification quality and positive region lead to different kinds of reduction anomalies in variable precision rough set model （VPRS-Model）. The reason is that the size of condition classification changes with the reduction of condition attributes, besides, classification quality, positive region and lower approximation distribution do not change equivalently any more. A reduction model based on lower approximation distribution is defined to avoid all kinds of reduction anomalies and an interval reduction method is presented based on ordered discernibility matrix. At last, the application of 3 kinds of interval reduction model on Wine Dataset illustrates the relationship of different reduction models.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2013年第11期1010-1018,共9页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61173052)
湖南省自然科学基金项目(No.14JJ4007)资助
关键词
区间约简
约简模型
约简算法
Interval Reduction, Reduction Model, Reduction Algorithm
