Instance selection

Abstract

In this chapter, we consider instance selection as an important focusing task in the data reduction phase of knowledge discovery and data mining. First of all, we define a broader perspective on concepts and topics related with instance selection (Sect. 8.1). Due to the fact that instance selection has been distinguished over the years as two type of tasks, depending on the data mining method applied later, we clearly separate it into two processes: training set selection and prototype selection. Theses trends are explained in Sect. 8.2. Thereafter, and focusing on prototype selection, we present a unifying framework that covers existing properties obtaining as a result a complete taxonomy (Sect. 8.3). The description of the operation as the most well known and some recent instance and/or prototype selection methods are provided in Sect. 8.4. Advanced and recent approaches that incorporate novel solutions based of hybridizations with other types of data reduction techniques or similar solutions are collected in Sect. 8.5. Finally, we summarize example evaluation results for prototype selection in an exhaustive experimental comparative analysis in Sect. 8.6. © Springer International Publishing Switzerland 2015.