Proceedings of the Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support

Analytical and Experimental Study of Filter Feature Selection Algorithms for High-dimensional Datasets

Authors
Adrian Pino, Carlos Morell
Corresponding Author
Adrian Pino
Available Online October 2013.
DOI
https://doi.org/10.2991/.2013.42How to use a DOI?
Keywords
feature selection, filter strategy, high-dimensional datasets, supervised classification
Abstract
In this paper a new taxonomy for feature selection algorithms created for high-dimensional datasets is proposed. Also, several selectors are described, analyzed and evaluated. It was observed that the Cfs-SFS algorithm reached the best solutions in most of the cases. Nevertheless, its application in very high-dimensional datasets is not recommended due to its computational cost. Cfs-BARS, Cfs-IRU and MRMR algorithms have similar results to those of Cfs-SFS, but in a relatively lesser time. The INTERACT algorithm gets good solutions too, but its computational cost is higher if compared to the above mentioned. On the other hand, the QPFS and FSBMC algorithms reached the worst solutions.
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Proceedings
Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2013
ISBN
978-90-78677-86-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/.2013.42How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Adrian Pino
AU  - Carlos Morell
PY  - 2013/10
DA  - 2013/10
TI  - Analytical and Experimental Study of Filter Feature Selection Algorithms for High-dimensional Datasets
BT  - Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support
PB  - Atlantis Press
SP  - 339
EP  - 349
SN  - 1951-6851
UR  - https://doi.org/10.2991/.2013.42
DO  - https://doi.org/10.2991/.2013.42
ID  - Pino2013/10
ER  -