A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Exploring automatic music annotation with "acoustically-objective" tags
Proceedings of the international conference on Multimedia information retrieval
Semantic Annotation and Retrieval of Music and Sound Effects
IEEE Transactions on Audio, Speech, and Language Processing
Colorizing tags in tag cloud: a novel query-by-tag music search system
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Audio tags correspond to keywords that people use to describe different aspects of a music clip, such as the genre, mood, and instrumentation. With the explosive growth of digital music available on the Web, automatic audio tagging, which can be used to annotate unknown music or retrieve desirable music, is becoming increasingly important. This can be achieved by training a binary classifier for each tag based on the labeled music data. However, since social tags are usually assigned by people with different levels of musical knowledge, they inevitably contain noisy information. To address the noisy label problem, we propose a novel method that exploits the tag count information. By treating the tag counts as costs, we model the audio tagging problem as a cost-sensitive classification problem. The results of audio tag annotation and retrieval experiments show that the proposed approach outperforms our previous method, which won the MIREX 2009 audio tagging competition.