eng Science and Education Publishing Journal of Computer Sciences and Applications 2328-725X 2015-05-04 3 3 61 66 10.12691/jcsa-3-3-1 JCSA2015331 article Eli M. Dow dowem@clarkson.edu; pendertj@clarkson.edu 1 Tim Penderghest dowem@clarkson.edu; pendertj@clarkson.edu 2 IBM / Clarkson University, Potsdam NY, USA Clarkson University, Potsdam NY, USA 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. http://pubs.sciepub.com/jcsa/3/3/1/jcsa-3-3-1.pdf binary decision tree classifier code-generator AI