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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) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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