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

Many real world applications employ multivariate 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 aBayesian online multi-label classification framework (BOMC) which learns a probabilistic linear classifier. The likelihood is modeled by a graphical model similar to TrueSkillTM , 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 F1-score. Our experiments on Reuters1-v2 dataset show BOMC compares favorably to the state-of-the-art online learners in macroaveraged F1-score and training time.

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Xinhua Zhang, Thore Graepel, Ralf Herbrich
• Keywords: JMLR 2010, Many Real World, Multivariate Performance Measures, Probabilistic Linear Classifier