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Proceedings 2001 IEEE International Conference on Data Mining

Stability of feature selection algorithms

Content Provider	IEEE Xplore Digital Library
Author	Kalousis, A. Prados, J. Hilario, M.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Geneva Univ., Switzerland (Kalousis, A.; Prados, J.; Hilario, M.)
Abstract	With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally we show how stability profiles can support the choice of a feature selection algorithm.
File Size	244161
File Format	PDF
ISBN	0769522785
ISSN	15504786
DOI	10.1109/ICDM.2005.135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification algorithms Predictive models Probability distribution Testing Sampling methods Computer science Proteomics Error analysis Stability analysis Data mining
Content Type	Text
Resource Type	Article

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