期刊
ENTROPY
卷 15, 期 6, 页码 2288-2302出版社
MDPI
DOI: 10.3390/e15062288
关键词
multi-granulation; entropy; feature selection
资金
- Fundation of Science & Technology Department of Sichuan Province, China [2012GZ0061]
In the view of granular computing, some general uncertainty measures are proposed through single-granulation by generalizing Shannon's entropy. However, in the practical environment we need to describe concurrently a target concept through multiple binary relations. In this paper, we extend the classical information entropy model to a multi-granulation entropy model (MGE) by using a series of general binary relations. Two types of MGE are discussed. Moreover, a number of theorems are obtained. It can be concluded that the single-granulation entropy is the special instance of MGE. We employ the proposed model to evaluate the significance of the attributes for classification. A forward greedy search algorithm for feature selection is constructed. The experimental results show that the proposed method presents an effective solution for feature analysis.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据