Abstract—The problem of handling a class imbalanced
problem by modifying decision tree algorithm has received
widespread attention in recent years. A new splitting measure,
called class overlapping-balancing entropy (OBE), is
introduced in this paper that essentially pay attentions to all
classes equally. Each step, the proportion of each class is
balanced via the assigned weighted values. They not only
depend on equalizing each class, but they also take into account
the overlapping region between classes. The proportion of
weighted values corresponding to each class is used as the
component of Shannon's entropy for splitting the current
dataset. From the experimental results, OBE significantly
outperforms the conventional splitting measures like Gini
index, gain ratio and DCSM, which are used in the well-known
decision tree algorithms. It also exhibits superior performance
compared to AE and ME that are designed for handling the
class imbalanced problem specifically.
Index Terms—Classification problem, class imbalanced learning, class overlapping-balancing entropy, decision tree algorithm.
The authors are with the Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Artit Sagoolmuang and Krung Sinapiromsaran, "Decision Tree Algorithm with Class Overlapping-Balancing Entropy for Class Imbalanced Problem," International Journal of Machine Learning and Computing vol. 10, no. 3, pp. 444-451, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).