Learning and Applying Case Adaptation Rules for Classification: An Ensemble Approach

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.