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Authors: Verónica Bolón-Canedo ; Beatriz Remeseiro ; Noelia Sánchez-Maroño and Amparo Alonso-Betanzos

Affiliation: University of A Coruña, Spain

ISBN: 978-989-758-015-4

Keyword(s): Cost-based Feature Selection, Machine Learning, Filter Methods, Support Vector Machine.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: The proliferation of high-dimensional data in the last few years has brought a necessity to use dimensionality reduction techniques, in which feature selection is arguably the favorite one. Feature selection consists of detecting relevant features and discarding the irrelevant ones. However, there are some situations where the users are not only interested in the relevance of the selected features but also in the costs that they imply, e.g. economical or computational costs. In this paper an extension of the well-known ReliefF method for feature selection is proposed, which consists of adding a new term to the function which updates the weights of the features so as to be able to reach a trade-off between the relevance of a feature and its associated cost. The behavior of the proposed method is tested on twelve heterogeneous classification datasets as well as a real application, using a support vector machine (SVM) as a classifier. The results of the experimental study show that the a pproach is sound, since it allows the user to reduce the cost significantly without compromising the classification error. (More)

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Paper citation in several formats:
Bolón-Canedo, V.; Remeseiro, B.; Sánchez-Maroño, N. and Alonso-Betanzos, A. (2014). mC-ReliefF - An Extension of ReliefF for Cost-based Feature Selection.In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 42-51. DOI: 10.5220/0004756800420051

@conference{icaart14,
author={Verónica Bolón{-}Canedo. and Beatriz Remeseiro. and Noelia Sánchez{-}Maroño. and Amparo Alonso{-}Betanzos.},
title={mC-ReliefF - An Extension of ReliefF for Cost-based Feature Selection},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={42-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004756800420051},
isbn={978-989-758-015-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - mC-ReliefF - An Extension of ReliefF for Cost-based Feature Selection
SN - 978-989-758-015-4
AU - Bolón-Canedo, V.
AU - Remeseiro, B.
AU - Sánchez-Maroño, N.
AU - Alonso-Betanzos, A.
PY - 2014
SP - 42
EP - 51
DO - 10.5220/0004756800420051

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