Knowledge-based instance selection: A compromise between efficiency and versatility

Traditionally, each instance selection proposal applies the same selection criterion to any problem. However, the performance of such criteria depends on the input data and a single one is not sufficient to guarantee success over a wide range of environments. An option to adapt the selection criteria to the input data is the use of meta-learning to build knowledge-based systems capable to choose the most appropriate selection strategy among several available candidates. Nevertheless, there is not in the literature a theoretical framework that guides the design of instance selection techniques based on meta-learning. This paper presents a framework for this purpose as well as a case study in which the framework is instantiated and an experimental study is carried out to show that the meta-learning approach offers a good compromise between efficiency and versatility in instance selection.