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On Active Learning in Multi-label Classification

  • Klaus Brinker
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In conventional multiclass classification learning, we seek to induce a prediction function from the domain of input patterns to a mutually exclusive set of class labels. As a straightforward generalization of this category of learning problems, so-called multi-label classification allows for input patterns to be associated with multiple class labels simultaneously. Text categorization is a domain of particular relevance which can be viewed as an instance of this setting. While the process of labeling input patterns for generating training sets already constitutes a major issue in conventional classification learning, it becomes an even more substantial matter of relevance in the more complex multi-label classification setting. We propose a novel active learning strategy for reducing the labeling effort and conduct an experimental study on the well-known Reuters-21578 text categorization benchmark dataset to demonstrate the efficiency of our approach.

Keywords

Support Vector Machine Active Learning Class Label Input Pattern Kernel Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Klaus Brinker
    • 1
  1. 1.Data and Knowledge Engineering, Faculty of Computer ScienceOtto-von-Guericke-University MagdeburgMagdeburgGermany

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