Transplanting Binary Decision Trees

Eli M. Dow^1, and Tim Penderghest^2,

¹IBM / Clarkson University, Potsdam NY, USA

²Clarkson University, Potsdam NY, USA

Cite this paper:
Eli M. Dow and Tim Penderghest. Transplanting Binary Decision Trees. Journal of Computer Sciences and Applications. 2015; 3(3):61-66. doi: 10.12691/jcsa-3-3-1

Abstract

In this paper, we describe a means of compiling binary decision trees as generated by the C4.5 binary decision tree classifier into high-performance, reusable, stand-alone, run-time classifiers. We demonstrate the memory savings and run time characteristics of a compiled tree as compared to the traditional use of a C4.5 runtime. We demonstrate 100% correctness over every input we have available for testing as compared to our own enhanced version of the classic C4.5 run-time classification routine, consultr. In addition, this work provides a framework for comparing decision tree classifiers to more in vogue classifiers such as support vector machines as demonstrated within.

Keywords:
binary decision tree classifier code-generator AI

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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