Embedded Feature Selection for Multi-label Classification of Music Emotions

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 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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