Abstract
This paper introduces a genetic programming approach to the generation of classification prototypes. Prototype-based classification is a pattern recognition methodology in which the training set of a classification problem is represented by a small subset of instances. The assignment of labels to test instances is usually done by a 1NN rule. We propose a new prototype generation method, based on genetic programming, in which examples of each class are automatically combined to generate highly effective classification prototypes. The genetic program aims to maximize an estimate of the generalization performance of a 1NN classifier using the prototypes. We report experimental results on a benchmark for the evaluation of prototype generation methods. Experimental results show the validity of our approach: the proposed method outperforms most of the state of the art techniques when using both small and large data sets. Better results are obtained for data sets with numeric attributes only, although the performance of our method on mixed data is very competitive as well.
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Keywords
- Genetic Programming
- Generalization Performance
- Training Instance
- Initial Prototype
- Classification Prototype
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Escalante, H.J., Mendoza, K., Graff, M., Morales-Reyes, A. (2013). Genetic Programming of Prototypes for Pattern Classification. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_11
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DOI: https://doi.org/10.1007/978-3-642-38628-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
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