Relational-Tri-Training: Learning First-Order Rules Exploiting Unlabeled Data
Yanjuan Li · Jisuanji kexue yu tansuo · 2012
For the current inductive logic programming (ILP) system, the sufficient training datasets are required and the unlabeled data cannot be used. To solve this limitation, this paper introduces a first-order rule-learning algorithm exploiting the unlabeled data, named relational-tri-training (R-tri-training). This algorithm combines the tri-training based on propositional logic representation and ILP based on first-order logic representation, investigates the issue how to improve the performance of classifiers using the unlabeled data under the framework of ILP. Three different ILP systems are initialized according to the labeled data and the background knowledge, and then the three classifiers are refined by iteratively using the unlabeled data. That is, under special condition, the unlabeled data are going to be labeled to one classifier as the new training data when the same labeled results are given by the other two classifiers. Experimental results on the well-known benchmarks show that R-tri-training can effectively enhance the learning performance by exploiting the unlabeled data, and the performance of R-tri-training is better than genetic inductive logic programming (GILP), NFOIL, KFOIL and ALEPH.