Journal
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
Volume 5, Issue 4, Pages 668-678Publisher
SPRINGERNATURE
DOI: 10.1080/18756891.2012.718113
Keywords
Embedded feature selection; Multi-label learning; Music emotion
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Funding
- Natural Science Foundation of China [61005006]
- Fundamental Research Funds for the Central Universities
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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 LP-Chi, max and avg, 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.
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