Multi-view ANNs for Multi-relational Classification

Hongyu Guo, Herna L. Viktor

Abstract: Artificial neural networks (ANNs) provide a general, effective and practical approach for learning complex target functions. However, ANNs are not suitable for handling relational data, where information about the target concept is distributed over multiple related relations. ANNs algorithms usually only explore one relation, the so-called target relation, thus excluding crucial knowledge embedded in the related so-called background relations. This paper introduces a new approach, the multiple view artificial neural networks (MVNNs) method, to address the need for bridging the gap between ANNs and relational databases. The MVNNs strategy, firstly, propagates essential information held in the target relation to all background relations. Subsequently, it exploits multiple ANNs, which explore the target concepts against the separate back-ground relations. Thirdly, it incorporates crucial background knowledge, as obtained by the ANNs, into a meta-learning mechanism to construct the final model. Our experiments on eight data sets show that the MVNNs method achieves promising results in terms of overall accuracy obtained, when compared with two other relational data mining algorithms.