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