Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDIM.2008.4746761
Title: Learning classifiers without negative examples: A reduction approach
Authors: Zhang, D.
Lee, W.S. 
Issue Date: 2008
Source: Zhang, D.,Lee, W.S. (2008). Learning classifiers without negative examples: A reduction approach. 3rd International Conference on Digital Information Management, ICDIM 2008 : 638-643. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDIM.2008.4746761
Abstract: The problem of PU Learning, i.e., learning classifiers with positive and unlabelled examples (but not negative examples), is very important in information retrieval and data mining. We address this problem through a novel approach: reducing it to the problem of learning classifiers for some meaningful multivariate performance measures. In particular, we show how a powerful machine learning algorithm, Support Vector Machine, can be adapted to solve this problem. The effectiveness and efficiency of the proposed approach have been confirmed by our experiments on three real-world datasets. ©2008 IEEE.
Source Title: 3rd International Conference on Digital Information Management, ICDIM 2008
URI: http://scholarbank.nus.edu.sg/handle/10635/41603
ISBN: 9781424429172
DOI: 10.1109/ICDIM.2008.4746761
Appears in Collections:Staff Publications

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