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AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
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SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Negative training data can be harmful to text classification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Proceedings of the 7th ACM international conference on Web search and data mining
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With the amount of digitalized documents increasing exponentially, it is more difficult for users to keep up to date with the knowledge in their domain. In this paper, we present a framework named IFME (Information Filtering by Multiple Examples) in a digital library environment to help users identify the literature related to their interests by leveraging the Positive Unlabeled learning (PU learning). Using a few relevant documents provided by a user and considering the documents in an online database as unlabeled data (called U), it ranks the documents in U using a PU learning algorithm. From the experimental results, we found that while the approach performed well when a large set of relevant feedback documents were available, it performed relatively poor when the relevant feedback documents were few. We improved IFME by combining PU learning with under-sampling to tune the performance. Using Mean Average Precision (MAP), our experimental results indicated that with under-sampling, the performance improved significantly even when the size of P was small. We believe the PU learning based IFME framework brings insights to develop more effective digital library systems.