Fuzzy Entropy and Mutual Information

An implementation of the theory of fuzzy entropy and fuzzy mutual information.


Updated 13 Sep 2013

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Nowadays there are heaps of articles on the theory of fuzzy entropy and fuzzy mutual information. However, there is a clear significant lack for a Matlab implementation of these concepts. Based on numerous requests from students and researchers, I have prepared this code to simplify such concepts and give a tool that you can try directly. Of course, you may find heaps of different methods by which you may enhance the functionality of the code, so please feel free to inform me and the rest of any such updates overhere. Kindly, if you use this code then cite either of the following papers:

[1] R. N. Khushaba, A. Al-Jumaily, and A. Al-Ani, “Novel Feature Extraction Method based on Fuzzy Entropy and Wavelet Packet Transform for Myoelectric Control”, 7th International Symposium on Communications and Information Technologies ISCIT2007, Sydney, Australia, pp. 352 – 357.

[2] R. N. Khushaba, S. Kodagoa, S. Lal, and G. Dissanayake, “Driver Drowsiness Classification Using Fuzzy Wavelet Packet Based Feature Extraction Algorithm”, IEEE Transaction on Biomedical Engineering, vol. 58, no. 1, pp. 121-131, 2011.

[3] Ahmed Al-Ani, Rami N. Khushaba, "A Population Based Feature Subset Selection Algorithm Guided by Fuzzy Feature Dependency", AMLTA 2012, CCIS 322, pp. 430–438, 2012.

P.S: Let me know about bugs, if any.

Dr. Rami N. Khushaba

Cite As

Rami Khushaba (2023). Fuzzy Entropy and Mutual Information (https://www.mathworks.com/matlabcentral/fileexchange/31888-fuzzy-entropy-and-mutual-information), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2010b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

The estimation is more accurate now, you can even plug it with the well-known MRMR feature selection and try it instead of the C++ MI toolbox that comes with MRMR algorithm.

I_xx estimation corrected with an example showing the values of I_xx and H_x when we have two completely redundant features.

The estimation of I_Cxx and I_xx have been updated into a better version now. Old versions are also included but commented for future use. If you find any bugs kindly let me know.