A Comparative Evaluation of Sequential Feature Selection Algorithms

David W. Aha, Richard L. Bankert
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:1-7, 1995.

Abstract

Several recent machine learning publications demonstrate the utility of using feature selection algorithms in supervised learning tasks. Among these, sequential feature selection algorithms are receiving attention. The most frequently studied variants of these algorithms are forward and backward sequential selection. Many studies on supervised learning with sequential feature selection report applications of these algorithms, but do not consider variants of them that might be more appropriate for some performance tasks. This paper reports positive empirical results on such variants, and argues for their serious consideration in similar learning tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-aha95a, title = {A Comparative Evaluation of Sequential Feature Selection Algorithms}, author = {Aha, David W. and Bankert, Richard L.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {1--7}, 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/aha95a/aha95a.pdf}, url = {https://proceedings.mlr.press/r0/aha95a.html}, abstract = {Several recent machine learning publications demonstrate the utility of using feature selection algorithms in supervised learning tasks. Among these, sequential feature selection algorithms are receiving attention. The most frequently studied variants of these algorithms are forward and backward sequential selection. Many studies on supervised learning with sequential feature selection report applications of these algorithms, but do not consider variants of them that might be more appropriate for some performance tasks. This paper reports positive empirical results on such variants, and argues for their serious consideration in similar learning tasks.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T A Comparative Evaluation of Sequential Feature Selection Algorithms %A David W. Aha %A Richard L. Bankert %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-aha95a %I PMLR %P 1--7 %U https://proceedings.mlr.press/r0/aha95a.html %V R0 %X Several recent machine learning publications demonstrate the utility of using feature selection algorithms in supervised learning tasks. Among these, sequential feature selection algorithms are receiving attention. The most frequently studied variants of these algorithms are forward and backward sequential selection. Many studies on supervised learning with sequential feature selection report applications of these algorithms, but do not consider variants of them that might be more appropriate for some performance tasks. This paper reports positive empirical results on such variants, and argues for their serious consideration in similar learning tasks. %Z Reissued by PMLR on 01 May 2022.
APA
Aha, D.W. & Bankert, R.L.. (1995). A Comparative Evaluation of Sequential Feature Selection Algorithms. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:1-7 Available from https://proceedings.mlr.press/r0/aha95a.html. Reissued by PMLR on 01 May 2022.

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