SEMI-SUPERVISED EVALUATION OF CONSTRAINT SCORES FOR FEATURE SELECTION

Mariam Kalakech, Philippe Biela, Denis Hamad, Ludovic Macaire

2011

Abstract

Recent feature constraint scores, that analyse must-link and cannot-link constraints between learning samples, reach good performances for semi-supervised feature selection. The performance evaluation is generally based on classification accuracy and is performed in a supervised learning context. In this paper, we propose a semi-supervised performance evaluation procedure, so that both feature selection and classification take into account the constraints given by the user. Extensive experiments on benchmark datasets are carried out in the last section. They demonstrate the effectiveness of feature selection based on constraint analysis.

Paper Citation

in Harvard Style

Kalakech M., Biela P., Hamad D. and Macaire L. (2011). SEMI-SUPERVISED EVALUATION OF CONSTRAINT SCORES FOR FEATURE SELECTION . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 175-182. DOI: 10.5220/0003680001750182

in Bibtex Style

@conference{ncta11,
author={Mariam Kalakech and Philippe Biela and Denis Hamad and Ludovic Macaire},
title={SEMI-SUPERVISED EVALUATION OF CONSTRAINT SCORES FOR FEATURE SELECTION},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={175-182},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003680001750182},
isbn={978-989-8425-84-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - SEMI-SUPERVISED EVALUATION OF CONSTRAINT SCORES FOR FEATURE SELECTION
SN - 978-989-8425-84-3
AU - Kalakech M.
AU - Biela P.
AU - Hamad D.
AU - Macaire L.
PY - 2011
SP - 175
EP - 182
DO - 10.5220/0003680001750182