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Measuring the performance of evolutionary multi-objective feature selection for prediction of musical genres and styles

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2013

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Gesellschaft für Informatik e.V.

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The prediction of high-level music categories, such as genres, styles, or personal preferences, helps to organise music collections. The relevance of single audio features for automatic classification depends on a certain category. Relevant feature subsets for each classification task can be identified by means of feature selec- tion. Continuing our previous studies on multi-objective feature selection for music classification, in this work we measure an impact of evolutionary multi-objective fea- ture selection on classification performance and compare it to the baseline application without feature selection. As confirmed by statistical tests, the integration of evolu- tionary multi-objective feature selection leads to a significant increase of performance according to both evaluation criteria as well as to classification error. This holds for all four tested classification methods and six music categories.

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Vatolkin, Igor (2013): Measuring the performance of evolutionary multi-objective feature selection for prediction of musical genres and styles. INFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-614-5. pp. 3012-3025. Regular Research Papers. Koblenz. 16.-20. September 2013

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