Selecting prototypes in mixed incomplete data

Abstract

In this paper we introduce a new method for selecting prototypes with Mixed Incomplete Data (MID) object description, based on an extension of the Nearest Neighbor rule. This new rule allows dealing with functions that are not necessarily dual functions of distances. The introduced compact set editing method (CSE) constructs a prototype consistent subset, which is also subclass consistent. The experimental results show that CSE has a very nice computational behavior and effectiveness, reducing around 50% of prototypes without appreciable degradation on accuracy, in almost all databases with more than 300 objects. © Springer-Verlag Berlin Heidelberg 2005.