Uma K.V1,*, Appavu alias Balamurugan S2
Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 61-70, 2020, DOI:10.31209/2019.100000153
Abstract 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. More >
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