Resumen

SCHNITZER, Steffen; SCHMIDT, Sebastian; RENSING, Christoph  y  HARRIEHAUSEN-MIIHLBAUER, Bettina. Combining Active and Ensemble Learning for Efficient Classification of Web Documents. Polibits [online]. 2014, n.49, pp.39-46. ISSN 1870-9044.

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.

Palabras llave : Text classification; active learning; user feedback; ensemble learning.

        · texto en Inglés     · Inglés ( pdf )