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EFFECTIVENESS - Mahesh Pal, Paul M Mather, An assessment of the effectiveness of decision tree methods for land cover classification, Remote Sensing of Environment, Volume 86, Issue 4, 30 August 2003, Pages 554-565.

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Article

Transplanting Binary Decision Trees

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 Publishing

Cite 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.edu

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.

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