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Entropy 2013, 15(6), 2288-2302; doi:10.3390/e15062288

Multi-Granulation Entropy and Its Applications

* ,
School of Computer Science & Engineering, University of Electronic Science and Technology of China, Sichuan, Chengdu 611731, China
* Author to whom correspondence should be addressed.
Received: 2 April 2013 / Revised: 22 May 2013 / Accepted: 30 May 2013 / Published: 6 June 2013
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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.
Keywords: multi-granulation; entropy; feature selection multi-granulation; entropy; feature selection
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zeng, K.; She, K.; Niu, X. Multi-Granulation Entropy and Its Applications. Entropy 2013, 15, 2288-2302.

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