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.
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104889
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