Embedded Feature Selection for Multi-label Classification of Music Emotions

DOI

10.1080/18756891.2012.718113How to use a DOI?

Keywords

Embedded feature selection, Multi-label learning, Music emotion

Abstract

When detecting of emotions from music, many features are extracted from the original music data. However, there are redundant or irrelevant features, which will reduce the performance of classification models. Considering the feature problems, we propose an embedded feature selection method, called Multi-label Embedded Feature Selection (MEFS), to improve classification performance by selecting features. MEFS embeds classifier and considers the label correlation. Other three representative multi-label feature selection methods, known as and together with four multi-label classification algorithms, is included for performance comparison. Experimental results show that the performance of our MEFS algorithm is superior to those filter methods in the music emotion dataset.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Mingyu You
AU  - Jiaming Liu
AU  - Guo-Zheng Li
AU  - Yan Chen
PY  - 2012
DA  - 2012/08/01
TI  - Embedded Feature Selection for Multi-label Classification of Music Emotions
JO  - International Journal of Computational Intelligence Systems
SP  - 668
EP  - 678
VL  - 5
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2012.718113
DO  - 10.1080/18756891.2012.718113
ID  - You2012
ER  -