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26

ICML
2001
IEEE

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Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection

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Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection

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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.

Sanmay Das

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Fast Filter Method | Hybrid Algorithm | ICML 2001 | Machine Learning | Wrapper Methods |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2001
Where	ICML
Authors	Sanmay Das

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