Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
One-class svms for document classification
The Journal of Machine Learning Research
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Positive Example based learners reduce human annotation effort significantly by removing the burden of labeling the negative examples. Various methods have been proposed in literature for building classifiers using positive and unlabeled examples. However, we empirically observe that classification accuracy of the state of the art methods degrades significantly as the number of labeled positive examples decreases. In this paper, we propose an active learning based method to address this issue. The proposed method learns starting from a handful of positively labeled examples and a large number of unlabeled examples. Experimental results on benchmark datasets show that the proposed method performs better than the state of the art methods when the percentage of labeled positive examples is small.