Abstract
This paper studies the problem of building text classifiers using only positive and unlabeled examples. At present, many techniques for solving this problem were proposed, such as Biased-SVM which is the existing popular method and its classification performance is better than most of two-step techniques. In this paper, an improved iterative classification approach is proposed which is the extension of Biased-SVM. The first iteration of our developed approach is Biased-SVM and the next iterations are to identify confident positive examples from the unlabeled examples. Then an extra penalty factor is given to weight these confident positive examples error. Experiments show that it is effective for text classification and outperforms the Biased-SVM and other two step techniques.
Supported by the National Natural Science Foundation of China (10971223, 11071252), Chinese Universities Scientific Fund(2011JS039, 2012Y130).
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Ke, T., Yang, B., Zhen, L., Tan, J., Li, Y., Jing, L. (2012). Building High-Performance Classifiers Using Positive and Unlabeled Examples for Text Classification. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_21
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DOI: https://doi.org/10.1007/978-3-642-31362-2_21
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