Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning
Table 2: Link-based classification results.
['[EMPTY]', 'CiteSeer', 'Cora', 'PubMed']
We employ accuracy as the evaluation metric, which is defined as the ratio between the number of correct predictions and the number of total predictions (i.e. the number of testing examples). D-Learner consistently outperforms SL-ProPPR on all three datasets, and the relative improvements are about 1.5% to 5%. Compared with SL-SVM, D-Learner achieves better results on the Cora and PubMed datasets, with relative improvements about 12% and 6%, respectively. For the CiteSeer dataset, D-Learner’s performance is comparable to SL-SVM.