Multi-class Prediction Using Stochastic Logic Programs View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007

AUTHORS

Jianzhong Chen , Lawrence Kelley , Stephen Muggleton , Michael Sternberg

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. More... »

PAGES

109-124

Book

TITLE

Inductive Logic Programming

ISBN

978-3-540-73846-6
978-3-540-73847-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-73847-3_17

DOI

http://dx.doi.org/10.1007/978-3-540-73847-3_17

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042730493


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