Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches
In Multilabel Learning (MLL) each training instance is associated with a set of labels and the task is to learn a function that maps an unseen instance to its corresponding label set. In this paper, we present a suite of {--} MLL algorithm independent {--} post-processing techniques that utilize the conditional and directional label-dependences in order to make the predictions from any MLL approach more coherent and precise. We solve constraint optimization problem over the output produced by any MLL approach and the result is a refined version of the input predicted label set. Using proposed techniques, we show absolute improvement of 3{\%} on English News and 10{\%} on Chinese E-commerce datasets for P@K metric.
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