Abstract
This paper presents a novel feature space enriching (FSE) technique to address the problem of sparse and noisy feature space in email classification. The (FSE) technique employs two semantic knowledge bases to enrich the original sparse feature space, which results in more semantic-richer features. From the enriched feature space, the classification algorithms can learn improved classifiers. Naive Bayes and support vector machine are selected as the classification algorithms. Experiments on an enterprise email dataset have shown that the FSE technique is effective for improving the email classification performance.
This paper has been supported by China NSF project (No.60221120145) and Shanghai Science and Technology Foundation (under the granted project No.02DJ14045)
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Ye, Y., Ma, F., Rong, H., Huang, J.Z. (2004). Improved Email Classification through Enriched Feature Space. In: Li, Q., Wang, G., Feng, L. (eds) Advances in Web-Age Information Management. WAIM 2004. Lecture Notes in Computer Science, vol 3129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27772-9_49
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DOI: https://doi.org/10.1007/978-3-540-27772-9_49
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