Abstract
This paper investigates a new approach for training text classifiers when only a small set of positive examples is available together with a large set of unlabeled examples. The key feature of this problem is that there are no negative examples for learning. Recently, a few techniques have been reported are based on building a classifier in two steps. In this paper, we introduce a novel method for the first step, which cluster the unlabeled and positive examples to identify the reliable negative document, and then run SVM iteratively. We perform a comprehensive evaluation with other two methods, and show experimentally that it is efficient and effective.
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Zhang, B., Zuo, W. (2008). A Novel Reliable Negative Method Based on Clustering for Learning from Positive and Unlabeled Examples. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_37
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DOI: https://doi.org/10.1007/978-3-540-68636-1_37
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