The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, nous proposons un nouvel algorithme de sélection de caractéristiques pour la classification multi-classes. L'algorithme proposé est basé sur des modèles de mélange gaussien (GMM) des caractéristiques et utilise la distance entre les deux classes les moins séparables comme métrique pour la sélection des caractéristiques. Le système proposé a été testé avec une machine à vecteurs de support (SVM) pour la classification multiclasse de la musique. Les résultats montrent que le schéma de sélection de fonctionnalités proposé est supérieur aux schémas conventionnels.
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Tacksung CHOI, Sunkuk MOON, Young-cheol PARK, Dae-hee YOUN, Seokpil LEE, "A GMM-Based Feature Selection Algorithm for Multi-Class Classification" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 8, pp. 1584-1587, August 2009, doi: 10.1587/transinf.E92.D.1584.
Abstract: In this paper, we propose a new feature selection algorithm for multi-class classification. The proposed algorithm is based on Gaussian mixture models (GMMs) of the features, and it uses the distance between the two least separable classes as a metric for feature selection. The proposed system was tested with a support vector machine (SVM) for multi-class classification of music. Results show that the proposed feature selection scheme is superior to conventional schemes.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1584/_p
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@ARTICLE{e92-d_8_1584,
author={Tacksung CHOI, Sunkuk MOON, Young-cheol PARK, Dae-hee YOUN, Seokpil LEE, },
journal={IEICE TRANSACTIONS on Information},
title={A GMM-Based Feature Selection Algorithm for Multi-Class Classification},
year={2009},
volume={E92-D},
number={8},
pages={1584-1587},
abstract={In this paper, we propose a new feature selection algorithm for multi-class classification. The proposed algorithm is based on Gaussian mixture models (GMMs) of the features, and it uses the distance between the two least separable classes as a metric for feature selection. The proposed system was tested with a support vector machine (SVM) for multi-class classification of music. Results show that the proposed feature selection scheme is superior to conventional schemes.},
keywords={},
doi={10.1587/transinf.E92.D.1584},
ISSN={1745-1361},
month={August},}
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TY - JOUR
TI - A GMM-Based Feature Selection Algorithm for Multi-Class Classification
T2 - IEICE TRANSACTIONS on Information
SP - 1584
EP - 1587
AU - Tacksung CHOI
AU - Sunkuk MOON
AU - Young-cheol PARK
AU - Dae-hee YOUN
AU - Seokpil LEE
PY - 2009
DO - 10.1587/transinf.E92.D.1584
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2009
AB - In this paper, we propose a new feature selection algorithm for multi-class classification. The proposed algorithm is based on Gaussian mixture models (GMMs) of the features, and it uses the distance between the two least separable classes as a metric for feature selection. The proposed system was tested with a support vector machine (SVM) for multi-class classification of music. Results show that the proposed feature selection scheme is superior to conventional schemes.
ER -