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

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概要

• 論文の詳細を見る

• 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.

• （社）電子情報通信学会の論文
• 2009-08-01

著者

• YOUN Dae-Hee

  Department of Electronic Engineering, Yonsei University

• Youn Dae-hee

  Department Of Electrical And Electronic Engineering Yonsei University

• Park Young-cheol

  Division Of Information Technology Yonsei University

• CHOI Tacksung

  Department of Electrical and Electronic Engineering at Yonsei University

• MOON Sunkuk

  Department of Electrical and Electronic Engineering at Yonsei University

• LEE Seokpil

  Korea Electronics Technology Institute 9FL, Electronics Center

• Youn Dae‐hee

  Yonsei Univ. Seoul Kor

• Youn Dae-hee

  Yonsei University

• PARK Young-Cheol

  Yonsei University

• Lee Seokpil

  Korea Electronics Technology Institute 9fl Electronics Center

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