Abstract
Improving the accuracy of assigning new email messages to small folders can reduce the likelihood of users creating duplicate folders for some topics. In this paper we presented a hybrid classification model, PERC, and use the Enron Email Corpus to investigate the performance of kNN, SVM and PERC in a simulation of a real-time situation. Our results show that PERC is significantly better at assigning messages to small folders. The effects of different parameter settings for the classifiers are discussed.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ke, SW., Bowerman, C., Oakes, M. (2006). PERC: A Personal Email Classifier. In: Lalmas, M., MacFarlane, A., Rüger, S., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds) Advances in Information Retrieval. ECIR 2006. Lecture Notes in Computer Science, vol 3936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11735106_41
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DOI: https://doi.org/10.1007/11735106_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33347-0
Online ISBN: 978-3-540-33348-7
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