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Authors: David Dernoncourt 1 ; Blaise Hanczar 2 and Jean-Daniel Zucker 3

Affiliations: 1 Institut National de la Santé et de la Recherche Médicale, Université Pierre et Marie-Curie - Paris 6 and Institute of Cardiometabolism and Nutrition, France ; 2 Université Paris Descartes, France ; 3 Institut National de la Santé et de la Recherche Médicale, Université Pierre et Marie-Curie - Paris 6, Institute of Cardiometabolism and Nutrition and Institut de Recherche pour le Développement, France

ISBN: 978-989-758-018-5

Keyword(s): Feature Selection, Stability, Ensemble, Small Sample.

Related Ontology Subjects/Areas/Topics: Applications ; Bioinformatics and Systems Biology ; Ensemble Methods ; Feature Selection and Extraction ; Pattern Recognition ; Software Engineering ; Theory and Methods

Abstract: Feature selection is an important step when building a classifier. However, the feature selection tends to be unstable on high-dimension and small-sample size data. This instability reduces the usefulness of selected features for knowledge discovery: if the selected feature subset is not robust, domain experts can have little trust that they are relevant. A growing number of studies deal with feature selection stability. Based on the idea that ensemble methods are commonly used to improve classifiers accuracy and stability, some works focused on the stability of ensemble feature selection methods. So far, they obtained mixed results, and as far as we know no study extensively studied how the choice of the aggregation method influences the stability of ensemble feature selection. This is what we study in this preliminary work. We first present some aggregation methods, then we study the stability of ensemble feature selection based on them, on both artificial and real data, as well as the resulting classification performance. (More)

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Paper citation in several formats:
Dernoncourt, D.; Hanczar, B. and Zucker, J. (2014). Stability of Ensemble Feature Selection on High-Dimension and Low-Sample Size Data - Influence of the Aggregation Method.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 325-330. DOI: 10.5220/0004922203250330

@conference{icpram14,
author={David Dernoncourt. and Blaise Hanczar. and Jean{-}Daniel Zucker.},
title={Stability of Ensemble Feature Selection on High-Dimension and Low-Sample Size Data - Influence of the Aggregation Method},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={325-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004922203250330},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Stability of Ensemble Feature Selection on High-Dimension and Low-Sample Size Data - Influence of the Aggregation Method
SN - 978-989-758-018-5
AU - Dernoncourt, D.
AU - Hanczar, B.
AU - Zucker, J.
PY - 2014
SP - 325
EP - 330
DO - 10.5220/0004922203250330

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