PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Feature Selection in a Cartesian Ensemble of Feature Subspace Classifiers for Music Categorisation
Rudolf Mayer, Andreas Rauber, Pedro J. Ponce de León, Carlos Pérez-Sancho and José Iñesta
In: Proceedings of ACM Multimedia Workshop on Music and Machine Learning (MML 2010) (2010) ACM , USA , pp. 53-56. ISBN 978-1-4503-0161-9

Abstract

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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7156
Deposited By:Carlos Pérez-Sancho
Deposited On:07 March 2011