1IBM / Clarkson University, Potsdam NY, USA
2Clarkson University, Potsdam NY, USA
Journal of Computer Sciences and Applications.
2015,
Vol. 3 No. 3, 61-66
DOI: 10.12691/jcsa-3-3-1
Copyright © 2015 Science and Education PublishingCite this paper: Eli M. Dow, Tim Penderghest. Transplanting Binary Decision Trees.
Journal of Computer Sciences and Applications. 2015; 3(3):61-66. doi: 10.12691/jcsa-3-3-1.
Correspondence to: Tim Penderghest, Clarkson University, Potsdam NY, USA. Email:
dowem@clarkson.edu; pendertj@clarkson.eduAbstract
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.
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