Case-Based Learning and Reasoning Using Layered Boundary Multigraphs

Instance-based and case-based learning algorithms learn by remembering instances. When scaling such approaches to datasets of sizes that are typically faced in today’s data-rich and data-driven decade, basic approaches to case retrieval and case learning quickly come to their limits. In this paper, we introduce a novel scalable algorithm for both, the retrieval and the retain phase of the CBR cycle. Our approach builds an efficient graph-based data structure when learning new cases which it exploits in a stochastic any-time manner during retrieval. We investigate its characteristics both, theoretically and empirically using established benchmark datasets as well as a specific larger-scale dataset.