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Thresholding Strategies for Large Scale Multi-Label Text Classifier

Abstract

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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Category:
Conference activity
Type:
materiały konferencyjne indeksowane w Web of Science
Title of issue:
Human System Interaction (HSI), 2013 The 6th International Conference on strony 350 - 355
Language:
English
Publication year:
2013
Bibliographic description:
Draszawka K., Szymański J..: Thresholding Strategies for Large Scale Multi-Label Text Classifier, W: Human System Interaction (HSI), 2013 The 6th International Conference on, 2013, IEEE,.
DOI:
Digital Object Identifier (open in new tab) 10.1109/hsi.2013.6577846
Verified by:
Gdańsk University of Technology

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