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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2012 Vol.2(5): 540-543 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.184

A Comparison of Efficiency and Robustness of ID3 and C4.5 Algorithms Using Dynamic Test and Training Data Sets

Payam Emami Khoonsari and AhmadReza Motie
Abstract—In the machine learning world making a decision is very important. Several approaches have been invented for doing so. Among the most efficient ones is the decision tree. ID3 and C4.5 algorithms have been introduced by J.R Quinlan which produce reasonable decision trees. In this paper we evaluate robustness of these algorithms against the training and test data set changes. At first an introduction has been presented, in the second part, we take a look at the algorithms and finally unique experimentations and findings are submitted.

Index Terms—ID3 algorithm, C4.5 algorithm, ID3 and C4.5 comparison, robustness of ID3 and C4.5, an empirical comparison of ID3 and C4.5.

Payam Emami Khoonsari is a master student in bioinformatics, University of Tampere, Finland (e-mail: payam.emamy@ gmail.com).
AhmadReza Motie is a lecturer in Jahad daneshgahi institute of higher education Esfahan, Iran (e-mail: motiee@acecr.ac.ir).


Cite:Payam Emami Khoonsari and AhmadReza Motie, "A Comparison of Efficiency and Robustness of ID3 and C4.5 Algorithms Using Dynamic Test and Training Data Sets," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 540-543, 2012.

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