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A unified formulation of entropy and its application

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  • Balakrishnan, Narayanaswamy
  • Buono, Francesco
  • Longobardi, Maria

Abstract

In this paper, a general formulation of entropy is proposed. It depends on two parameters and includes Shannon, Tsallis and fractional entropy, all as special cases. This measure of information is referred to as fractional Tsallis entropy and some of its properties are then studied. Furthermore, the corresponding entropy in the context of Dempster–Shafer theory of evidence is proposed and referred to as fractional version of Tsallis–Deng entropy. Finally, an application to two classification problems is presented.

Suggested Citation

  • Balakrishnan, Narayanaswamy & Buono, Francesco & Longobardi, Maria, 2022. "A unified formulation of entropy and its application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    • Handle: RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122002011
    DOI: 10.1016/j.physa.2022.127214
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    References listed on IDEAS

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    1. Calì, Camilla & Longobardi, Maria & Ahmadi, Jafar, 2017. "Some properties of cumulative Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1012-1021.
    2. Kumar, Vikas, 2016. "Some results on Tsallis entropy measure and k-record values," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 667-673.
    3. Madan Mohan Sati & Nitin Gupta, 2015. "Some Characterization Results on Dynamic Cumulative Residual Tsallis Entropy," Journal of Probability and Statistics, Hindawi, vol. 2015, pages 1-8, October.
    4. Qiuya Gao & Tao Wen & Yong Deng, 2021. "Information Volume Fractal Dimension," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 29(08), pages 1-9, December.
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    Cited by:

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    2. Li, Siran & Xiao, Fuyuan, 2023. "Normal distribution based on maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

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