Multi-Task Label Propagation with Dissimilarity Measures
published: Oct. 25, 2016, recorded: August 2016, views: 990
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Multi-task algorithms typically use task similarity information as a bias to speed up learning. We argue that, when the classification problem is unbalanced, task dissimilarity information provides a more effective bias, as rare class labels tend to be better separated from the frequent class labels. In particular, we show that a multi-task extension of the label propagation algorithm for graph-based classification works much better on protein function prediction problems when the task relatedness information is represented using a dissimilarity matrix as opposed to a similarity matrix.
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