In order to extend the capabilities of case-based reasoning (CBR), we implemented an ensemble for case-based reasoning (E4CBR) approach where an ensemble of CBR classifiers is combined with clustering and feature selection. We first select a subset of features of all the cases, and then cluster the cases into disjoint groups, where each group of cases forms the case-base of one of the member classifiers. Finally, in each case-base, a subset of features is ‘locally’ selected individually. To predict the label of an unseen case, each classifier in the ensemble provides a prediction, and the aggregation component of E4CBR combines the predictions by weighing each classifier using a CBR approach—a classifier with more cases similar to the test case receives a higher weight.We evaluated E4CBR on four publicly available biological data sets, and also compared the classification error of E4CBR with a single CBR classifier. In our experiments, we use TA3—a computational framework for CBR systems. Our results show that E4CBR reduces the classification error of our CBR classifier. On the basis of empirical results, our aggregation method outperforms the existing CBR aggregation methods. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 164-171 DOI: 10.1002/widm.22