A relational approach to probabilistic classification in a transductive setting

Abstract

Transduction is an inference mechanism adopted from several classification algorithms capable of exploiting both labeled and unlabeled data and making the prediction for the given set of unlabeled data only. Several transductive learning methods have been proposed in the literature to learn transductive classifiers from examples represented as rows of a classical double-entry table (or relational table). In this work we consider the case of examples represented as a set of multiple tables of a relational database and we propose a new relational classification algorithm, named TRANSC, that works in a transductive setting and employs a probabilistic approach to classification. Knowledge on the data model, i.e., foreign keys, is used to guide the search process. The transductive learning strategy iterates on a k-NN based re-classification of labeled and unlabeled examples, in order to identify borderline examples, and uses the relational probabilistic classifier Mr-SBC to bootstrap the transductive algorithm. Experimental results confirm that TRANSC outperforms its inductive counterpart (Mr-SBC).