LAUSR

Multilabel classification deals with instances assigned with multiple labels simultaneously. It focuses on learning a mapping from feature space to label a space for out-of-sample extrapolation. The mapping can be seen as a feature selection process in the feature domain or as a classifier training process in the classifier domain. The existing methods do not effectively learn the mapping when combining these two domains together. In this article, we derive a mechanism to extract label-specific features in local and global levels. We also derive a mechanism to train label-specific classifiers in individual and joint levels. Extracting features globally and training classifiers jointly can be seen as a dual process of learning the mapping function on two domains in a coarse-tuned way, while extracting features locally and training classifiers individually can be seen as a dual process of learning the mapping function on two domains in a fine-tuned way. The two-level feature selection and the two-level classifier training are derived to make the entire mapping learning process robust. Finally, extensive experimental results on several benchmarks under four domains are presented to demonstrate the effectiveness of the proposed approach.