Integrating Rule Induction and Case-Based Reasoning
to Enhance Problem Solving

Aijun An, Nick Cercone and Christine Chan

University of Regina, Regina, Saskatchewan, Canada S4S 0A2


Abstract. We present a new method that integrates rule induction and
case-based reasoning. The method is new in two aspects. First, it applies
a novel feature weighting function for assessing similarities between cases.
By using this weighting function, optimal case retrieval is achieved in that
the most relevant cases can be retrieved from the case base. Second, the
method handles both classification and numeric prediction tasks under a
mixed paradigm of rule-based and case-based reasoning. Before problem
solving, rule induction is performed to induce a set of decision rules from
a set of training data. The rules are then employed to determine some
parameters in the new weighting function. The induced rules are also
used to detect possible noise in the training set so that noisy cases are not
used in case-based reasoning. For classification tasks, rules are applied to
make decisions; if there is a conflict between matched rules, case-based
reasoning is performed. The method was implemented in ELEM2-CBR, a
learning and problem solving system. We demonstrate the performance
of ELEM2-CBR by comparing it with other methods on a number of
designed and real-world problems.
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