Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications
Abstract
Multi-layer perceptrons and trained classification trees are two very different techniques which have recently become popular. Given enough data and time, both methods are capable of performing arbi(cid:173) trary non-linear classification. We first consider the important differences between multi-layer perceptrons and classification trees and conclude that there is not enough theoretical basis for the clear(cid:173) cut superiority of one technique over the other. For this reason, we performed a number of empirical tests on three real-world problems in power system load forecasting, power system security prediction, and speaker-independent vowel identification. In all cases, even for piecewise-linear trees, the multi-layer perceptron performed as well as or better than the trained classification trees.
Cite
Text
Atlas et al. "Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications." Neural Information Processing Systems, 1989.Markdown
[Atlas et al. "Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications." Neural Information Processing Systems, 1989.](https://mlanthology.org/neurips/1989/atlas1989neurips-performance/)BibTeX
@inproceedings{atlas1989neurips-performance,
title = {{Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications}},
author = {Atlas, Les E. and Cole, Ronald A. and Connor, Jerome T. and El-Sharkawi, Mohamed A. and Ii, Robert J. Marks and Muthusamy, Yeshwant K. and Barnard, Etienne},
booktitle = {Neural Information Processing Systems},
year = {1989},
pages = {622-629},
url = {https://mlanthology.org/neurips/1989/atlas1989neurips-performance/}
}