Extending Case Adaptation with Automatically-Generated Ensembles of Adaptation Rules

Case-based regression often relies on simple case adaptation methods. This paper investigates new approaches to enriching the adaptation capabilities of case-based regression systems, based on the use of ensembles of adaptation rules generated from the case base. The paper explores both local and global methods for generating adaptation rules from the case base, and presents methods for ranking the generated rules and combining the resulting ensemble of adaptation rules to generate new solutions. It tests these methods in five standard domains, evaluating their performance compared to four baseline methods, standard k-NN, linear regression, locally weighted linear regression, and an ensemble of k-NN predictors with different feature subsets. The results demonstrate that the proposed method generally outperforms the baselines and that the accuracy of adaptation based on locally-generated rules is highly competitive with that of global rule-generation methods with much greater computational cost.