Talks and Poster Presentations (with Proceedings-Entry):
R. Mayer, A. Rauber, P.J. Ponce de Leon, C. Pérez-Sancho, J. Iñesta:
"Feature selection in a cartesian ensemble of feature subspace classifiers for music categorisation";
Talk: ACM Multimedia 2010 - Workshop on Machine Learning and Music,
2010-10-25; in: "Proceedings of International workshop on Machine learning and music (MML '10)",
In this paper, we evaluate the impact of feature selection on the classification accuracy and the achieved dimensionality reduction, which benefits the time needed on training classification models. Our classification scheme therein is a Cartesian ensemble classification system, based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. We use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on Music IR benchmark datasets. We show that while feature selection does not benefit classification accuracy, it greatly reduces the dimensionality of each feature subspace, and thus adds to great gains in the time needed to train the individual classification models that form the ensemble.
Musical genre classiﬁcation, ensemble classiﬁcation, feature selection, feature reduction
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.