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|Title:||Learning to classify texts using positive and unlabeled data||Authors:||Li, X.
|Issue Date:||2003||Citation:||Li, X.,Liu, B. (2003). Learning to classify texts using positive and unlabeled data. IJCAI International Joint Conference on Artificial Intelligence : 587-592. ScholarBank@NUS Repository.||Abstract:||In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a particular class (called positive class) and a set U of unlabeled documents that contains documents from class P and also other types of documents (called negative class documents), we want to build a classifier to classify the documents in U into documents from P and documents not from P. The key feature of this problem is that there is no labeled negative document, which makes traditional text classification techniques inapplicable. In this paper, we propose an effective technique to solve the problem. It combines the Rocchio method and the SVM technique for classifier building. Experimental results show that the new method outperforms existing methods significantly.||Source Title:||IJCAI International Joint Conference on Artificial Intelligence||URI:||http://scholarbank.nus.edu.sg/handle/10635/114659||ISSN:||10450823|
|Appears in Collections:||Staff Publications|
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