Cautious Collective Classification
The Journal of Machine Learning Research
On cross-validation and stacking: building seemingly predictive models on random data
ACM SIGKDD Explorations Newsletter
Collective classification using heterogeneous classifiers
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
An analysis of how ensembles of collective classifiers improve predictions in graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Active learning and inference method for within network classification
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Collective classification techniques jointly infer all class labels of a relational data set, using the inferences about one class label to influence inferences about related class labels. Kou and Cohen recently introduced an efficient relational model based on stacking that, despite its simplicity, has equivalent accuracy to more sophisticated joint inference approaches. Using experiments on both real and synthetic data, we show that the primary cause for the performance of the stacked model is the reduction in bias from learning the stacked model on inferred labels rather than true labels. The reduction in variance due to conditional inference also contributes to the effect but it is not as strong. In addition, we show that the performance of the joint inference and stacked learners can be attributed to an implicit weighting of local and relational features at learning time.