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.
Reference Key
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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