Multi-class Prediction Using Stochastic Logic Programs

Authors:
Jianzhong Chen;Lawrence Kelley;Stephen Muggleton;Michael Sternberg
Affiliations:
Department of Computing, Imperial College London, London SW7 2AZ, UK;Department of Biological Sciences, Imperial College London, London SW7 2AZ, UK;Department of Computing, Imperial College London, London SW7 2AZ, UK;Department of Biological Sciences, Imperial College London, London SW7 2AZ, UK
Venue:
Inductive Logic Programming
Year:
2007

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Abstract

In this paper, we present a probabilistic method of dealing with multi-class classification using Stochastic Logic Programs (SLPs), a Probabilistic Inductive Logic Programming (PILP) framework that integrates probability, logic representation and learning. Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have multiple predictions. We apply an SLP parameter estimation algorithm to a previous study in the protein fold prediction area and a multi-class classification working example, in which logic programs have been learned by Inductive Logic Programming (ILP) and a large number of multiple predictions have been detected. On the basis of several experiments, we demonstrate that PILP approaches (eg. SLPs) have advantages for solving multi-class prediction problems with the help of learned probabilities. In addition, we show that SLPs outperform ILP plus majority class predictor in both predictive accuracy and result interpretability.