Journal Article

Univariate Decision Tree Induction using Maximum Margin Classification

Olcay Taner Yıldız

in The Computer Journal

Published on behalf of British Computer Society

Volume 55, issue 3, pages 293-298
Published in print March 2012 | ISSN: 0010-4620
Published online March 2011 | e-ISSN: 1460-2067 | DOI: https://dx.doi.org/10.1093/comjnl/bxr020
Univariate Decision Tree Induction using Maximum Margin Classification

Show Summary Details

Preview

In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier performs at least as good as C4.5 and linear discriminant tree (LDT) with a similar time complexity. For two-class data sets, it generates significantly smaller trees than C4.5 and LDT without sacrificing from accuracy, and generates significantly more accurate trees than C4.5 and LDT for multiclass data sets with one-vs-rest methodology.

Keywords: statistical learning theory; decision trees

Journal Article.  0 words. 

Subjects: Computer Science

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content. subscribe or login to access all content.