Accurate and fast prototype selection based on the notion of relevant and border prototypes.

In supervised classification, a training set is given to a classifier to learn a decision rule for classifying unseen cases. When large training sets are processed, the training stage becomes slow especially for instance-based learning. However, not all information in a training set is useful for classification because it could contain either redundant or noisy prototypes. Therefore a process for discarding useless prototypes is required; this process is known as prototype selection. In this work, we present some methods for selecting prototypes based on prototype relevance, which are accurate and fast for large datasets; in addition, our methods can be applied over datasets described by nominal features. We report experimental results showing the effectiveness of our methods as well as a comparison against other successful prototype selection methods. [ABSTRACT FROM AUTHOR]