document: Large scale multi-label classification via metalabeler The explosion of online content has made the management of such content non-trivial . Web-related tasks such as web page categorization , news filtering , query categorization , tag recommendation , etc. often involve the construction of multi-label categorization systems on a large scale . Existing multi-label classification methods either do not scale or have unsatisfactory performance . In this work , we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive cross-validation . Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods . Moreover , MetaLabeler scales to millions of multi-labeled instances and can be deployed easily . This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo ! , yielding a significant improvement in performance .
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