Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
Abstract
In this paper, we examine the advantages and disadvantages of filter and wrapper methods for feature selection and propose a new hybrid algorithm that uses boosting and incorporates some of the features of wrapper methods into a fast filter method for feature selection. Empirical results are reported on six real-world datasets from the UCI repository, showing that our hybrid algorithm is competitive with wrapper methods while being much faster, and scales well to datasets with thousands of features.
Cite
Text
Das. "Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection." International Conference on Machine Learning, 2001.Markdown
[Das. "Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection." International Conference on Machine Learning, 2001.](https://mlanthology.org/icml/2001/das2001icml-filters/)BibTeX
@inproceedings{das2001icml-filters,
title = {{Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection}},
author = {Das, Sanmay},
booktitle = {International Conference on Machine Learning},
year = {2001},
pages = {74-81},
url = {https://mlanthology.org/icml/2001/das2001icml-filters/}
}