Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning
Table 3: Parameter tuning for link-based classification.
['[EMPTY]', 'Citeseer Range', 'Citeseer Optimal', 'Cora Range', 'Cora Optimal', 'PubMed Range', 'PubMed Optimal']
Instead of adding a weight to control one constraint’s effect, we adopt a more straightforward way which tunes the example number of a constraint to use. lpFailure2 is found helpful on all three datasets. The mutual-exclusivity constraint of text view is helpful on Cora and PubMed datasets. Cotraining classifiers of both citation view and text content view does not improve the results, it is because, in the three datesets, the average citation number of publications is about 1.5 to 2.5, which is too sparse to train a reliable citation view classifier.