A Comparative Evaluation of Sequential Feature Selection Algorithms
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
Text
Aha and Bankert. "A Comparative Evaluation of Sequential Feature Selection Algorithms." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Aha and Bankert. "A Comparative Evaluation of Sequential Feature Selection Algorithms." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/aha1995aistats-comparative/)BibTeX
@inproceedings{aha1995aistats-comparative,
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},
year = {1995},
pages = {1-7},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/aha1995aistats-comparative/}
}