Ensemble of Adaptations for Classification: Learning Adaptation Rules for Categorical Features

Abstract

Acquiring knowledge for case adaptation is a classic challenge for case-based reasoning (CBR). To provide CBR systems with adaptation knowledge, machine learning methods have been developed for automatically generating adaptation rules. An influential approach uses the case difference heuristic (CDH) to generate rules by comparing pairs of cases in the case base. The CDH method has been studied for case-based prediction of numeric values (regression) from inputs with primarily numeric features, and has proven effective in that context. However, previous work has not attempted to apply the CDH method to classification tasks, to generate rules for adapting categorical solutions. This paper introduces an approach to applying the CDH to cases with categorical features and target values, based on the generalized case value difference heuristic (GCVDH). It also proposes a classification method using ensembles of GCVDH-generated rules, ensemble of adaptations for classification (EAC), an extension to our previous work on ensembles of adaptations for regression (EAR). It reports on an evaluation comparing the accuracy of EAC to three baseline methods on four standard domains, as well as comparing EAC to an ablation relying on single adaptation rules, and assesses the effect of training/test size on accuracy. Results are encouraging for the effectiveness of the GCVDH approach and for the value of applying ensembles of learned adaptation rules for classification.