Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
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Partially Supervised Classification of Text Documents
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PEBL: positive example based learning for Web page classification using SVM
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IEEE Transactions on Knowledge and Data Engineering
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Neural Computation
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Building a Text Classifier by a Keyword and Unlabeled Documents
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Editorial: Classifying text streams by keywords using classifier ensemble
Data & Knowledge Engineering
Similarity-based approach for positive and unlabelled learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
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Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training examples be missing, the number of positive examples available for learning may also be fairly limited due to the impracticality of hand-labeling a large number of training examples. Current PU learning techniques have focused mostly on identifying reliable negative instances from the unlabeled set U. In this paper, we address the oft-overlooked PU learning problem when the number of training examples in the positive set Pis small. We propose a novel technique LPLP (Learning from Probabilistically Labeled Positive examples) and apply the approach to classify product pages from commercial websites. The experimental results demonstrate that our approach outperforms existing methods significantly, even in the challenging cases where the positive examples in Pand the hidden positive examples in Uwere not drawn from the same distribution.