Robust Feature Selection Using Ensemble Feature Selection Techniques
Abstract
Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, where multiple feature selection methods are combined to yield more robust results. We show that these techniques show great promise for high-dimensional domains with small sample sizes, and provide more robust feature subsets than a single feature selection technique. In addition, we also investigate the effect of ensemble feature selection techniques on classification performance, giving rise to a new model selection strategy.
Cite
Text
Saeys et al. "Robust Feature Selection Using Ensemble Feature Selection Techniques." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87481-2_21Markdown
[Saeys et al. "Robust Feature Selection Using Ensemble Feature Selection Techniques." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/saeys2008ecmlpkdd-robust/) doi:10.1007/978-3-540-87481-2_21BibTeX
@inproceedings{saeys2008ecmlpkdd-robust,
title = {{Robust Feature Selection Using Ensemble Feature Selection Techniques}},
author = {Saeys, Yvan and Abeel, Thomas and Van de Peer, Yves},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {313-325},
doi = {10.1007/978-3-540-87481-2_21},
url = {https://mlanthology.org/ecmlpkdd/2008/saeys2008ecmlpkdd-robust/}
}