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Repozytorium publikacji
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Thresholding Strategies for Large Scale Multi-Label Text Classifier

This article presents an overview of thresholding methods for labeling objects given a list of candidate classes’ scores. These methods are essential to multi-label classification tasks, especially when there are a lot of classes which are organized in a hierarchy. Presented techniques are evaluated using the state-of-the-art dedicated classifier on medium scale text corpora extracted from Wikipedia. Obtained results show that the classification performance can be improved with the use of new class-specific thresholding methods, which set decision values depending on each candidate class separately

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