Machine Learning Department
School of Computer Science, Carnegie Mellon University


Notes on Stacked Graphical Learning for
Efficient Inference in Markov Random Fields

Zhenzhen Kou, William W. Cohen

January 2007


Keywords: Machine learning, stacked graphical learning, collective classification

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 instance'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. We also give theoretical analysis to better understand the algorithm.

18 pages

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