A Monotonic Measure for Optimal Feature Selection
Abstract
Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distancebased measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification.
Cite
Text
Liu et al. "A Monotonic Measure for Optimal Feature Selection." European Conference on Machine Learning, 1998. doi:10.1007/BFB0026678Markdown
[Liu et al. "A Monotonic Measure for Optimal Feature Selection." European Conference on Machine Learning, 1998.](https://mlanthology.org/ecmlpkdd/1998/liu1998ecml-monotonic/) doi:10.1007/BFB0026678BibTeX
@inproceedings{liu1998ecml-monotonic,
title = {{A Monotonic Measure for Optimal Feature Selection}},
author = {Liu, Huan and Motoda, Hiroshi and Dash, Manoranjan},
booktitle = {European Conference on Machine Learning},
year = {1998},
pages = {101-106},
doi = {10.1007/BFB0026678},
url = {https://mlanthology.org/ecmlpkdd/1998/liu1998ecml-monotonic/}
}