Combining Active and Ensemble Learning for Efficient Classification of Web Documents

 

Steffen Schnitzer¹, Sebastian Schmidt¹, Christoph Rensing¹, and Bettina Harriehausen-Miihlbauer²

 

¹ Multimedia Communications Lab, Technische Universitat Darmstadt, Germany (e-mail: Steffen.Schnitzer@kom.tu-darmstadt.de, Sebastian.Schmidt@kom.tu-darmstadt.de, Christoph.Rensing@kom.tu-darmstadt.de).

² University of Applied Sciences, Darmstadt, Germany (e-mail: Bettina.Harriehausen@h-da.de).

 

Manuscript received on December 17, 2013
Accepted for publication on February 6, 2014.

 

Abstract

Classification of text remains a challenge. Most machine learning based approaches require many manually annotated training instances for a reasonable accuracy. In this article we present an approach that minimizes the human annotation effort by interactively incorporating human annotators into the training process via active learning of an ensemble learner. By passing only ambiguous instances to the human annotators the effort is reduced while maintaining a very good accuracy. Since the feedback is only used to train an additional classifier and not for re-training the whole ensemble, the computational complexity is kept relatively low.

Key words: Text classification, active learning, user feedback, ensemble learning.

 

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Acknowledgements

The work presented in this paper was partly funded by the German Federal Ministry of Education and Research (BMBF) under grant no. 01IS12054 and partially funded in the framework of Hessen Modell Projekte, financed with funds of LOEWE-State Offensive for the Development of Scientific and Economic Excellence (HA project no. 292/11-37). The responsibility for the contents of this publication lies with the authors. We thank kimeta GmbH for the essential help assisting with building the evaluation corpus.

 

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Note

The first two authors contributed equally to this work.
¹ http://www.google.com
² http://www.bing.com