Bayesian Online Learning for Multi-label and Multi-variate Performance Measures


Xinhua Zhang, Thore Graepel, Ralf Herbrich ;
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:956-963, 2010.


Many real world applications employ multi-variate performance measures and each example can belong to multiple classes. The currently most popular approaches train an SVM for each class, followed by ad hoc thresholding. Probabilistic models using Bayesian decision theory are also commonly adopted. In this paper, we propose a Bayesian online multi-label classification framework (BOMC) which learns a probabilistic linear classifier. The likelihood is modeled by a graphical model similar to TrueSkill^TM, and inference is based on Gaussian density filtering with expectation propagation. Using samples from the posterior, we label the testing data by maximizing the expected F_1-score. Our experiments on Reuters1-v2 dataset show BOMC compares favorably to the state-of-the-art online learners in macro-averaged F_1-score and training time.

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