Novel top-down approaches for hierarchical classification and their application to automatic music genre classification
Carlos Nascimento Silla Junior, Alex Alves Freitas · 2009
This paper presents two novel hierarchical classification methods which are extensions of a previously proposed selective classifier top-down approach, which consists of selecting - during the training phase - the best classifier at each node of a classifier tree. More precisely, we propose two novel selective top-down hierarchical methods. First, a method that selects the best feature set instead of the best classifier. Secondly, a method that selects both the best classifier and the best representation simultaneously. These methods are evaluated on the task of hierarchical music genre classification using four different types of feature sets extracted from each song and four classifiers.