Affiliations: [a] Facultad de Ciencias de la Computación, Autonomous University of Puebla, Puebla, Mexico
| [b] Coordinación de Ciencias Computacionales, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico
Correspondence:
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Corresponding author. J. Arturo Olvera-López, Facultad de Ciencias de la Computación, Autonomous University of Puebla, Puebla, Mexico. E-mail: [email protected].
Abstract: 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.
