|
|||||
|
|
| 多粒度云模型的相似性度量 | |
| 引用本文： | 杨洁,王国胤,张清华,冯林.多粒度云模型的相似性度量[J].模式识别与人工智能,2018,31(8):677-692. |
| 作者姓名： | 杨洁 王国胤 张清华 冯林 |
| 作者单位： | 1.重庆邮电大学 计算智能重庆市重点实验室 重庆 400065 2.遵义师范学院 物理与电子科学学院 遵义 563002 3.四川师范大学 计算机科学学院 成都 610101 |
| 基金项目： | 国家自然科学基金项目(No.61572091,61472056)、高端人才项目(No.RC2016005)、贵州省联合基金项目(No.黔科合LH字[2017]7075号)、四川省科技支撑计划项目(No.2015GZ0079)、省级重点学科(No.黔学科办[2013]18号)资助 |
| 摘 要： | 当前存在的云模型相似性度量仅局限于单粒度空间,缺乏多粒度云模型的相似性度量的相关研究.因此,文中首先证明知识距离框架的相关性质,并建立知识距离与信息度量、信息粒度之间的联系,在分层递阶粒结构上得到如下结论:同一粒结构中粒空间的粒度差异正相关于知识距离,通过知识距离可将随粒度连续变化的粒空间映射到一维坐标上.最后,在知识距离框架的基础上提出云模型相似性度量方法.实验验证上述结论在云模型粒空间上成立. |
| 关 键 词： | 云模型 知识距离 层次粒结构 相似性度量 |
| 收稿时间： | 2018-04-15 |
Similarity Measure of Multi-granularity Cloud Model |
|
| YANG Jie,WANG Guoyin,ZHANG Qinghua,FENG Lin.Similarity Measure of Multi-granularity Cloud Model[J].Pattern Recognition and Artificial Intelligence,2018,31(8):677-692. | |
| Authors: | YANG Jie WANG Guoyin ZHANG Qinghua FENG Lin |
| Affiliation: | 1.Chongqing Key Laboratory of Computational Intelligence, Ch-ongqing University of Post and Telecommunications, Chong-qing 400065 2.School of Physics and Electronic Science, Zunyi Normal University, Zunyi 563002 3.School of Computer Science, Sichuan Normal University, Chengdu 610101 |
| Abstract: | Traditional cloud similarity measures are only suitable for single granularity, and the multi-granularity cloud similarity measure is insufficient in research. In this paper, a knowledge distance framework is proposed and its relative properties are proved. The relationships between knowledge distance and information measure and information granularity are established. Moreover, two valuable conclusions are drawn in a hierarchical granular structure. The granularity difference of two granular spaces in a hierarchical granular structure is positive correlation to the knowledge distance framework between them. The granular spaces change continuously with the granularity and they can be mapped into the one-dimension coordinate. Finally, a similarity measure of cloud model based on the knowledge distance framework(KDFCM) is proposed. The experiments verify that the properties of KDFCM are consistent with the above conclusions. |
| Keywords: | Cloud Model Knowledge Distance Hierarchical Granular Structure Similarity Measure |
| 点击此处可从《模式识别与人工智能》浏览原始摘要信息 | |
| 点击此处可从《模式识别与人工智能》下载全文 | |