On the Handling of Continuous-Valued Attributes in Decision Tree Generation
Abstract
We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present empirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains.
Cite
Text
Fayyad and Irani. "On the Handling of Continuous-Valued Attributes in Decision Tree Generation." Machine Learning, 1992. doi:10.1007/BF00994007Markdown
[Fayyad and Irani. "On the Handling of Continuous-Valued Attributes in Decision Tree Generation." Machine Learning, 1992.](https://mlanthology.org/mlj/1992/fayyad1992mlj-handling/) doi:10.1007/BF00994007BibTeX
@article{fayyad1992mlj-handling,
title = {{On the Handling of Continuous-Valued Attributes in Decision Tree Generation}},
author = {Fayyad, Usama M. and Irani, Keki B.},
journal = {Machine Learning},
year = {1992},
pages = {87-102},
doi = {10.1007/BF00994007},
volume = {8},
url = {https://mlanthology.org/mlj/1992/fayyad1992mlj-handling/}
}