Enhancing case-based regression with automatically-generated ensembles of adaptations

Instance-based methods have been successfully applied to numerical prediction (regression) tasks in many domains. Such methods often rely on a simple combination function to generate a prediction from past instances. Case-based reasoning for regression adds a richer case adaptation step to adjust prior solutions to fit new problems. This article presents a new approach to case adaptation for case-based regression systems, based on applying an ensemble of case adaptation rules generated automatically from pairs of cases in the case base, using the case difference heuristic . It evaluates the method’s performance, considering in particular the effects of using local versus global case information to generate adaptation rules from the case base. Experimental results support that the proposed method generally outperforms baselines and that the accuracy of adaptation based on locally-generated rules is highly competitive with that of global rule-generation methods considering many more cases.