IEICE TRANSACTIONS on Information
A GMM-Based Feature Selection Algorithm for Multi-Class Classification
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.8 pp.1584-1587
- Publication Date
- 2009/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.1584
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
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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 -
English
