Introduction to the special issue on learning from multi-label data
Machine Learning
Multi-label ensemble based on variable pairwise constraint projection
Information Sciences: an International Journal
Multilabel Learning via Random Label Selection for Protein Subcellular Multilocations Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Neighborhood rough sets based multi-label classification for automatic image annotation
International Journal of Approximate Reasoning
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Audio tags correspond to keywords that people use to describe different aspects of a music clip. 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. Our method that won the MIREX 2009 audio tagging competition is one of this kind of methods. However, since social tags are usually assigned by people with different levels of musical knowledge, they inevitably contain noisy information. By treating the tag counts as costs, we can model the audio tagging problem as a cost-sensitive classification problem. In addition, tag correlation information is useful for automatic audio tagging since some tags often co-occur. By considering the co-occurrences of tags, we can model the audio tagging problem as a multi-label classification problem. To exploit the tag count and correlation information jointly, we formulate the audio tagging task as a novel cost-sensitive multi-label (CSML) learning problem and propose two solutions to solve it. The experimental results demonstrate that the new approach outperforms our MIREX 2009 winning method.