Vatolkin, IgorHorbach, Matthias2019-03-072019-03-072013978-3-88579-614-5https://dl.gi.de/handle/20.500.12116/20715The 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.enText/Conference Paper1617-5468