A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions

This paper presents a novel method for multi-relational classification via an aggregation-based Inductive Logic Programming (ILP) approach. We extend the classical ILP representation by aggregation of multiple-features which aid the classification process by allowing for the analysis of relationships and dependencies between different features. In order to efficiently learn rules of this rich format, we present a novel algorithm capable of performing aggregation with the use of virtual joins of the data. By using more expressive aggregation predicates than the existential quantifier used in standard ILP methods, we improve the accuracy of multi-relational classification. This claim is supported by experimental evaluation on three different real world datasets.

• Type: Conference
• Year: 2007
• Where: PKDD
• Authors: Richard Frank, Flavia Moser, Martin Ester
• Keywords: Classical Ilp Representation, Multi-relational Classification, PKDD 2007, Standard Ilp Methods