Perks of Being Lazy: Boosting Retrieval Performance

Abstract

Case-Based Reasoning (CBR) is a lazy learning method and, being such, when a new query is made to a CBR system, the swiftness of its retrieval phase proves to be very important for the overall system performance. The availability of ubiquitous data today is an opportunity for CBR systems as it implies more cases to reason with. Nevertheless, this availability also introduces a challenge for the CBR retrieval since distance calculations become computationally expensive. A good example of a domain where the case base is subject to substantial growth over time is the health records of patients where a query is typically an incremental update to prior cases. To deal with the retrieval performance challenge in such domains where cases are sequentially related, we introduce a novel method which significantly reduces the number of cases assessed in the search of exact nearest neighbors (NNs). In particular, when distance measures are metrics, they satisfy the triangle inequality and our method leverages this property to use it as a cutoff in NN search. Specifically, the retrieval is conducted in a lazy manner where only the cases that are true NN candidates for a query are evaluated. We demonstrate how a considerable number of unnecessary distance calculations is avoided in synthetically built domains which exhibit different problem feature characteristics and different cluster diversity.