1. Mayer, R., Rauber, A., Ponce de León, P.J., Pérez-Sancho, C., Iñesta, J.M.
"Feature Selection in a Cartesian Ensemble of Feature Subspace Classifiers for Music Categorisation"
, vol. Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010), pp. 53--56. Florence (Italy) (2010)
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 en-
semble 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.