Genre classification for million song dataset using confidence-based classifiers combination

Authors:
Yajie Hu;Mitsunori Ogihara
Affiliations:
University of Miami, Coral Gables, FL, USA;University of Miami, Coral Gables, FL, USA
Venue:
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Year:
2012

Citing 1
Cited 1

Learning to combine discriminative classifiers: confidence based

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Classification accuracy is not enough

Journal of Intelligent Information Systems

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Abstract

We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided.