Abstract
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 datasets show that the novel margin tree classifier performs at least as good as C4.5 and LDT with a similar time complexity. For two class datasets it generates smaller trees than C4.5 and LDT without sacrificing from accuracy, and generates significantly more accurate trees than C4.5 and LDT for multiclass datasets with one-vs-rest methodology.
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Yildiz, O.T. (2011). Univariate Margin Tree. In: Gelenbe, E., Lent, R., Sakellari, G., Sacan, A., Toroslu, H., Yazici, A. (eds) Computer and Information Sciences. Lecture Notes in Electrical Engineering, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9794-1_3
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DOI: https://doi.org/10.1007/978-90-481-9794-1_3
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