Robust Linear Discriminant Trees
Abstract
We present a new method for the induction of classification trees with linear discriminants as the partitioning function at each internal node. This paper presents two main contributions: first, a novel objective function called soft entropy which is used to identify optimal coefficients for the linear discriminants, and second, a novel method for removing outliers called iter ative re-filtering which boosts performance on many datasets. These two ideas are presented in the context of a single learning algorithm called DT-SEPIR.
Cite
Text
John. "Robust Linear Discriminant Trees." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[John. "Robust Linear Discriminant Trees." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/john1995aistats-robust/)BibTeX
@inproceedings{john1995aistats-robust,
title = {{Robust Linear Discriminant Trees}},
author = {John, George H.},
booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
year = {1995},
pages = {285-291},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/john1995aistats-robust/}
}