Boundary expansion algorithm of a decision tree induction for an imbalanced dataset

Abstract

A decision tree is one of the famous classifiers based on a recursive partitioning algorithm. This paper introduces the Boundary Expansion Algorithm (BEA) to improve a decision tree induction that deals with an imbalanced dataset. BEA utilizes all attributes to define non-splittable ranges. The computed means of all attributes for minority instances are used to find the nearest minority instance, which will be expanded along all attributes to cover a minority region. As a result, BEA can successfully cope with an imbalanced dataset comparing with C4.5, Gini, asymmetric entropy, top-down tree, and Hellinger distance decision tree on 25 imbalanced datasets from the UCI Repository.

Keywords

• C4.5
• decision tree
• classification
• boundary expansion algorithm