1BC: A First-Order Bayesian Classifier

Peter Flach and Nicolas Lachiche

University of Bristol, United Kingdom
{flach,lachiche}@cs.bris.ac.uk



Abstract. In this paper we present iBO, a first-order Bayesian Classifier. 
Our approach is to view individuals as structured terms, and to distinguish 
between structural predicates referring to subterms (e.g. atoms
from molecules), and properties applying to one or several of these subterms (
e.g. a bond between two atoms). We describe an individual in
terms of elementary features consisting of zero or more structural predicates 
and one property; these features are considered conditionally independent 
following the usual naive Bayes assumption. 1BC has been implemented 
in the context of the first-order descriptive learner Tertius,
and we describe several experiments demonstrating the viability of our
approach.
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