Entropy partition method and its application for discrete variables and continuous variables

Entropy partition method for complex system has been applied in many ldnds of fields. In this paper, we improve the calculation of correlative measure for both discrete variables and continuous variables, and apply this method in vascular endothelial dysfunction (ED) discrete data and neuro-endocrine-immune (NEI) continuous data respectively. The partition results show this entropy partition methodpsilas broad availability and obvious advantage in dealing with complex, multiple, nonlinear data.

[1]  Anil K. Seth,et al.  Consciousness and Complexity , 2022 .

[2]  Jianqiang Yi,et al.  One Entropy Partition Method for TCM , 2006, 2006 International Conference on Computational Intelligence and Security.

[3]  Li,et al.  Correlation Analysis between TCM Syndromes and Physicochemical Parameters , 2006 .

[4]  Tommy W. S. Chow,et al.  Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information , 2005, IEEE Transactions on Neural Networks.

[5]  K. Torkkola,et al.  Nonlinear feature transforms using maximum mutual information , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[6]  R. S. Jackson,et al.  Evaluation of the performance of dual chevron bluff body vortex flowmeters , 1996 .

[7]  R. Ross.  The pathogenesis of atherosclerosis: a perspective for the 1990s , 1993, Nature.

[8]  Wentian Li.  Mutual information functions versus correlation functions , 1990 .

[9]  J. Nathanson,et al.  Immunological function of the blood-cerebrospinal fluid barrier. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[10]  H. Besedovsky,et al.  Network of immune-neuroendocrine interactions. , 1977, Clinical and experimental immunology.

[11]  Roger C. Conant,et al.  Laws of Information which Govern Systems , 1976, IEEE Transactions on Systems, Man, and Cybernetics.