.png){kind=icon}
返回
Accelerator for multi-granularity attribute reduction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2019.04.014.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
By considering the information granulation in Granular Computing, the concept of the multi granularity is important. It is mainly because different results of information granulation will imply different levels of granularity. Nevertheless, multi-granularity has been paid less attention to the problem of attribute reduction in rough set which is regarded as one of the most important mathematical tools in Granular Computing. Therefore, how to search the multi-granularity reduct will be mainly explored in this paper. Different from the previous studies which generate reduct by using one and only one granularity, multi-granularity reduct is actually a set of the reducts derived from multiple levels of granularity. A natural way for computing multi-granularity reduct is to repeat the process of searching reduct for each level of granularity. Obviously, such an approach is timeconsuming. To fill such a gap, an acceleration strategy is introduced into the process of searching multi-granularity reduct. Our acceleration strategy can be respectively realized through considering two variations of granularity: 1) from a finer granularity to a coarser granularity; 2) from a coarser granularity to a finer granularity. Such two variations indicate that the reduct related to previous granularity may have guidance on the computation of reduct related to the present granularity. Consequently, two accelerators are designed for speeding up the process of finding multi-granularity reduct. The experimental results over 16 UCI data sets show that our accelerators can not only reduce the elapsed time of searching attributes significantly, but also select attributes which will not contribute to a poorer classification performance. This study suggests new trends concerning the problem of attribute reduction and the corresponding searching strategy. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Accelerator
Approximation quality
Attribute reduction
Conditional entropy
Multi-granularity
Rough set
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
分享收藏Improving awareness in early stages of security analysis: A zone partition method based on GrC.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏A Group Incremental Approach to Feature Selection Applying Rough Set Technique应用粗糙集技术的组增量式特征选择方法
PREAI
分享收藏
分享收藏分享收藏
查看更多内容