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Filters, wrappers and a boosting-based hybrid for feature selection,” (2001)
| Venue: | inProceedings of the 8th International Conference on Machine Learning, |
| Citations: | 107 - 0 self |
BibTeX
@INPROCEEDINGS{Das01filters,wrappers,
author = {Sanmay Das},
title = {Filters, wrappers and a boosting-based hybrid for feature selection,”},
booktitle = {inProceedings of the 8th International Conference on Machine Learning,},
year = {2001},
pages = {74--81}
}
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Abstract
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.
