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

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2017
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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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boonchuay2017boundarysongklanakarin Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Boonchuay, Kesinee;Sinapiromsaran, Krung;Lursinsap, Chidchanok;
Journal songklanakarin journal of science and technology (sjst)
Year 2017
DOI
DOI not found
URL
http://rdo.psu.ac.th/sjstweb/journal/39-5/39-5-13.pdf
Keywords
chemistry Biology (General) Technology (General) Engineering (General). Civil engineering (General) Technology Science (General) Science Medical technology physics telecommunication electronic computers. computer science

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