Oblique Decision Tree Algorithm with Minority Condensation for Class Imbalanced Problem

Abstract

In recent years, a significant issue in classification is to handle a dataset containing imbalanced number of instances in each class. Classifier modification is one of the well-known techniques to deal with this particular issue. In this paper, the effective classification model based on an oblique decision tree is enhanced to work with the imbalanced datasets that is called oblique minority condensed decision tree (OMCT). Initially, it selects the best axis-parallel hyperplane based on decision tree algorithm using the minority entropy of instances within the minority inner fence selection. Then it perturbs this hyperplane along each axis to improve its minority entropy. Finally, it stochastically perturbs this hyperplane to escape the local solution. From the experimental results, OMCT significantly outperforms 6 state-of-the-art decision tree algorithms that are CART, C4.5, OC1, AE, DCSM and ME on 18 real-world datasets from UCI in term of precision, recall and F1 score. Moreover, the size of decision tree from OMCT is significantly smaller than others.