Multi-Granulation Entropy and Its Applications
AbstractIn 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.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Zeng, K.; She, K.; Niu, X. Multi-Granulation Entropy and Its Applications. Entropy 2013, 15, 2288-2302.
Zeng K, She K, Niu X. Multi-Granulation Entropy and Its Applications. Entropy. 2013; 15(6):2288-2302.Chicago/Turabian Style
Zeng, Kai; She, Kun; Niu, Xinzheng. 2013. "Multi-Granulation Entropy and Its Applications." Entropy 15, no. 6: 2288-2302.