Competence Region Modelling in Relational Classification

Abstract

Relational classification is a promising branch of machine learning techniques for classification in networked environments which does not fulfil the iid assumption (independent and identically distributed). During the past few years, researchers have proposed many relational classification methods. However, almost none of them was able to work efficiently with large amounts of data or sparsely labelled networks. It is introduced in this paper a new approach to relational classification based on competence region modelling. The approach aims at solving large relational data classification problems, as well as seems to be a reasonable solution for classification of sparsely labelled networks by decomposing the initial problem to subproblems (competence regions) and solve them independently. According to preliminary results obtained from experiments performed on real world datasets competence region modelling approach to relational classification results with more accurate classification than standard approach.