Theses and Dissertations

Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Dr. Dongchul Kim

Second Advisor

Dr. Bin Fu

Third Advisor

Dr. Xiang Lian


Discretization algorithm is a crucial step to not only achieve summarization of continuous attributes but also better performance in classification that requires discrete values as input. In this thesis, I propose a supervised discretization method, Global Entropy Based Greedy algorithm, which is based on the Information Entropy Minimization. Experimental results show that the proposed method outperforms state of the art methods with well-known benchmarking datasets. To further improve the proposed method, a new approach for stop criterion that is based on the change rate of entropy was also explored. From the experimental analysis, it is noticed that the threshold based on the decreasing rate of entropy could be more effective than a constant number of intervals in the classification such as C5.0.


Copyright 2016 Sai Jyothsna Jonnalagadda. All Rights Reserved.