ABSTRACT OF THE THESIS

RULE REFINEMENT IN INDUCTIVE KNOWLEDGE-BASED 
SYSTEMS

by
Mohamed Arteimi



This thesis presents empirical methods for enhancing the accuracy of inductive learning systems. It 
addresses the problems of: learning propositional production rules in multi-class classification 
tasks in noisy domains, maintaining continuous learning when confronted with new situations after 
the initial learning phase is completed, and classifying an object when no rule is satisfied for it.

It is shown that interleaving the learning and performance-evaluation processes allows accurate 
classifications to be made on real-world data sets. The thesis presents the system ARIS which 
implements this approach, and it is shown that the resulting classifications are often more accurate 
than those made by the non-refined knowledge bases. 

The core design decision that lies behind ARIS is that it employs an ordering of the rules 
according to their weight. A rule’s weight is learned by using Bayes’ theorem to calculate weights 
for the rule’s conditions and combining them. This model focuses the analysis of the knowledge 
base and assists the refinement process significantly.

The system is non-interactive, it relies on heuristics to focus the refinement on those experiments 
that appear to be most consistent with the refinement data set. The design framework of ARIS 
consists of a tabular model for expressing rule weights, and the relationship between refinement 
cases and the rules satisfied for each case to focus the refinement process. The system has been 
used to refine knowledge bases created by ARIS itself, as well as to refine knowledge bases 
created by the RIPPER and C4.5 systems in ten selected domains.