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C5.0 Decision Tree Model Using Tsallis Entropy and Association Function for General and Medical Dataset

Uma K.V1,*, Appavu alias Balamurugan S2

1 Department of Information Technology, Thiagarajar College of Engineering, Tiruparankundram, Madurai-625015, Tamilnadu, India.
2 Research Director and Professor, Department of Computer Science and Engineering, E.G.S.Pillay Engineering College, Nagapattinam, Tamilnadu, India.

* Corresponding Author: K.V.Uma, email

Intelligent Automation & Soft Computing 2020, 26(1), 61-70.


Real world data consists of lot of impurities. Entropy measure will help to handle impurities in a better way. Here, data selection is done by using Naïve Bayes’ theorem. The sample which has posterior probability value greater than that of the threshold value is selected. C5.0 decision tree classifier is taken as base and modified the Gain calculation function using Tsallis entropy and Association function. The proposed classifier model provides more accuracy and smaller tree for general and Medical dataset. Precision value obtained for Medical dataset is more than that of existing method.


Cite This Article

U. K.V and A. A. B. S, "C5.0 decision tree model using tsallis entropy and association function for general and medical dataset," Intelligent Automation & Soft Computing, vol. 26, no.1, pp. 61–70, 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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