Sustainable case learning for continuous domains

Case-based reasoning (CBR) provides an adequate framework to cope with continuous domains, where a great amount of new valuable experiences are generated in a non-stop way. CBR systems become more competent in their evolution over time by means of learning new relevant experiences. There are two central problems derived from the continuous nature of some domains: the fast growing size of the case library and the overhead in the case library organisation. Our proposal to overcome these two problems is to learn only relevant cases, and to establish a lazy learning algorithm for storing cases in the case library. A relevance measure based on LʹEixample distance, and a related ontology of cases are defined, and the lazy learning algorithm is described. Finally, experimental tests on real data are presented and discussed.