A GMM-Based Feature Selection Algorithm for Multi-Class Classification

Tacksung CHOI
Sunkuk MOON
Young-cheol PARK
Dae-hee YOUN
Seokpil LEE

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D    No.8    pp.1584-1587
Publication Date: 2009/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1584
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
classification,  feature selection,  GMM separability index,  

Full Text: PDF(150.3KB)>>
Buy this Article

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.