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Efficient training on biased minimax probability machine for imbalanced text classification

9 years 10 months ago
Efficient training on biased minimax probability machine for imbalanced text classification
The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks. In this paper, we propose a Second Order Cone Programming (SOCP) based algorithm to train the model. We outline the theoretical derivatives of the biased classification model, and address the text classification tasks where negative training documents significantly outnumber the positive ones using the proposed strategy. We evaluated the learning scheme in comparison with traditional solutions on three different datasets. Empirical results have shown that our method is more effective and robust to handle imbalanced text classification problems. Categories and Subject Descriptors
Xiang Peng, Irwin King
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2007
Where WWW
Authors Xiang Peng, Irwin King
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