Transductive Relational Classification in the Co-training Paradigm

Consider a multi-relational database, to be used for classification, that contains a large number of unlabeled data. It follows that the cost of labeling such data is prohibitive. Transductive learning, which learns from labeled as well as from unlabeled data already known at learning time, is highly suited to address this scenario. In this paper, we construct multi-views from a relational database, by considering different subsets of the tables as contained in a multi-relational database. These views are used to boost the classification of examples in a co-training schema. The automatically generated views allow us to overcome the independence problem that negatively affect the performance of co-training methods. Our experimental evaluation empirically shows that co-training is beneficial in the transductive learning setting when mining multi-relational data and that our approach works well with only a small amount of labeled data.