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
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: email@example.com).
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.