PruDent: A Pruned and Confident Stacking Approach for Multi-label Classification
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PruDent: A Pruned and Confident Stacking Approach for Multi-label Classification
Abstract— PruDent: A Pruned and Confident Stacking Approach for Multi-label Classification. Over the past decade or so, several research groups have addressed the problem of multi-label classification where each example can belong to more than one class at the same time. A common approach, called Binary Relevance BR, addresses this problem by inducing a separate classifier for each class. < Final Year Projects 2016 > Research has shown that this framework can be improved if mutual class dependence is exploited: an example that belongs to class X is likely to belong also to class Y ; conversely, belonging to X can make an example less likely to belong to Z. Several works sought to model this information by using the vector of class labels as additional example attributes.
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