Robust Linear Discriminant Trees

George H. John
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:285-291, 1995.

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 this Paper

BibTeX
@InProceedings{pmlr-vR0-john95a, title = {Robust Linear Discriminant Trees}, author = {John, George H.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {285--291}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/john95a/john95a.pdf}, url = {https://proceedings.mlr.press/r0/john95a.html}, 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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Robust Linear Discriminant Trees %A George H. John %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-john95a %I PMLR %P 285--291 %U https://proceedings.mlr.press/r0/john95a.html %V R0 %X 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. %Z Reissued by PMLR on 01 May 2022.
APA
John, G.H.. (1995). Robust Linear Discriminant Trees. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:285-291 Available from https://proceedings.mlr.press/r0/john95a.html. Reissued by PMLR on 01 May 2022.

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