Stable and Accurate Feature Selection
Abstract
In addition to accuracy, stability is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number of samples in a data set is small and the dimensionality is high. In this study, we introduce a stability measure, and perform both accuracy and stability measurements of MRMR (Minimum Redundancy Maximum Relevance) feature selection algorithm on different data sets. The two feature evaluation criteria used by MRMR, MID (Mutual Information Difference) and MIQ (Mutual Information Quotient), result in similar accuracies, but MID is more stable. We also introduce a new feature selection criterion, MID _ α , where redundancy and relevance of selected features are controlled by parameter α .
Cite
Text
Gulgezen et al. "Stable and Accurate Feature Selection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04180-8_47Markdown
[Gulgezen et al. "Stable and Accurate Feature Selection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/gulgezen2009ecmlpkdd-stable/) doi:10.1007/978-3-642-04180-8_47BibTeX
@inproceedings{gulgezen2009ecmlpkdd-stable,
title = {{Stable and Accurate Feature Selection}},
author = {Gulgezen, Gokhan and Cataltepe, Zehra and Yu, Lei},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {455-468},
doi = {10.1007/978-3-642-04180-8_47},
url = {https://mlanthology.org/ecmlpkdd/2009/gulgezen2009ecmlpkdd-stable/}
}