Multi-Granulation Entropy and Its Applications
Abstrak
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.
Topik & Kata Kunci
Penulis (3)
Kai Zeng
Kun She
Xinzheng Niu
Akses Cepat
- Tahun Terbit
- 2013
- Sumber Database
- DOAJ
- DOI
- 10.3390/e15062288
- Akses
- Open Access ✓