PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian Online Learning for Multi-label and Multi-variate Performance Measures
X Zhang, T Graepel and R Herbrich
In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)(2010).

Abstract

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 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 F1-score. Our experiments on Reuters1-v2 dataset show BOMC compares favorably to the state-of-the-art online learners in macro-averaged F1-score and training time.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Natural Language Processing
Theory & Algorithms
ID Code:7465
Deposited By:Wray Buntine
Deposited On:17 March 2011