A Cartesian Ensemble of Feature Subspace Classifiers for Music Categorization (pdf)
Thomas Lidy, Rudolf Mayer, Andreas Rauber, Pedro J. Ponce de León, Antonio Pertusa, and Jose Manuel Iñesta
Abstract: We present a cartesian ensemble classification system that is based on the principle of late fusion and feature sub- spaces. These feature subspaces describe different aspects of the same data set. The framework is built on the Weka machine learning toolkit and able to combine arbitrary fea- ture sets and learning schemes. In our scenario, 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 classifi- cation, based on numerous Music IR benchmark datasets, and evaluate a set of combination/voting rules. The results show that the approach is superior to the best choice of a single algorithm on a single feature set. Moreover, it also releases the user from making this choice explicitly.
An ensemble classification system built on top of Weka:
Results, using different datasets, classifiers and feature sets:
Execution times were about 10 seconds per song, so rather slow for large collections.
The ensemble approach delivered superior results through adding a reasonable amount of feature sets and classifiers. However, they did not discover a combination rule that always outperforms all the others.