Learning and Applying Case Adaptation Rules for Classification:
An Ensemble Approach
Abstract
The ability of case-based reasoning systems to
solve novel problems depends on their capability to
adapt past solutions to new circumstances. However, acquiring the knowledge required for case
adaptation is a classic challenge for CBR. This
motivates the use of machine learning to generate
adaptation knowledge. Much adaptation learning
research has studied the case difference heuristic
(CDH) approach, which generates adaptation rules
from pairs of cases in the case base by ascribing
observed differences in case solutions to the differences in the problems they solve, to generate rules
for adapting similar problem differences. Extensive research has successfully applied the CDH approach to adaptation rule learning for case-based
regression (numerical prediction) tasks. However,
classification tasks have been outside of its scope.
The work presented in this paper addresses that
gap by extending CDH-based learning of adaptation rules to apply to cases with categorical features and solutions. It presents the generalized case
value heuristic to assess case and solution differences and applies it in an ensemble-based casebased classification method, ensembles of adaptations for classification (EAC), built on the authors’
previous work on ensembles of adaptations for regression (EAR). Experimental results support the
effectiveness of EAC.