On positive and unlabeled learning for text classification

Authors:
T. István Nagy;Richárd Farkas;János Csirik
Affiliations:
University of Szeged, Department of Informatics, Szeged, Hungary;Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung, Stuttgart, Germany;MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
Venue:
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Year:
2011

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Abstract

In this paper we present a slightly modified machine learning approach for text classification working exclusively from positive and unlabeled samples. Our method can assure that the positive class is not underrepresented during the iterative training process and it can achieve 30% better F-value when the amount of positive examples is low.