Combining Active and Ensemble Learning for Efficient Classification of Web Documents

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.

Bibliographic Details

Main Authors:	Schnitzer,Steffen, Schmidt,Sebastian, Rensing,Christoph, Harriehausen-Miihlbauer,Bettina
Format:	Digital revista
Language:	English
Published:	Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo 2014
Online Access:	http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1870-90442014000100005
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