Error-Correcting Output Coding Corrects Bias and Variance
Abstract
Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k ≫ 2 classes. This paper presents an investigation of why the ECOC technique works, particularly when employed with decision-tree learning algorithms. It shows that the ECOC method— like any form of voting or committee—can reduce the variance of the learning algorithm. Furthermore—unlike methods that simply combine multiple runs of the same learning algorithm—ECOC can correct for errors caused by the bias of the learning algorithm. Experiments show that this bias correction ability relies on the non-local behavior of C4.5.
Cite
Text
Kong and Dietterich. "Error-Correcting Output Coding Corrects Bias and Variance." International Conference on Machine Learning, 1995. doi:10.1016/B978-1-55860-377-6.50046-3Markdown
[Kong and Dietterich. "Error-Correcting Output Coding Corrects Bias and Variance." International Conference on Machine Learning, 1995.](https://mlanthology.org/icml/1995/kong1995icml-error/) doi:10.1016/B978-1-55860-377-6.50046-3BibTeX
@inproceedings{kong1995icml-error,
title = {{Error-Correcting Output Coding Corrects Bias and Variance}},
author = {Kong, Eun Bae and Dietterich, Thomas G.},
booktitle = {International Conference on Machine Learning},
year = {1995},
pages = {313-321},
doi = {10.1016/B978-1-55860-377-6.50046-3},
url = {https://mlanthology.org/icml/1995/kong1995icml-error/}
}