Stacked Graphical Models for Efficient Inference in Markov Random Fields [chapter]

Zhenzhen Kou, William W. Cohen
2007 Proceedings of the 2007 SIAM International Conference on Data Mining  
In collective classification, classes are predicted simultaneously for a group of related instances, rather than predicting a class for each instance separately. Collective classification has been widely used for classification on relational datasets. However, the inference procedure used in collective classification usually requires many iterations and thus is expensive. We propose stacked graphical learning, a meta-learning scheme in which a base learner is augmented by expanding one
more » ... s features with predictions on other related instances. Stacked graphical learning is efficient, especially during inference, capable of capturing dependencies easily, and can be implemented with any kind of base learner. In experiments on eight datasets, stacked graphical learning is 40 to 80 times faster than Gibbs sampling during inference.
doi:10.1137/1.9781611972771.57 dblp:conf/sdm/KouC07 fatcat:irvkv5lrxbdx3bloimexw72vfe