An iterative genetic programming approach to proto

Authors: José María ValenciaRamírez Mario Graff Hugo Jair Escalante Jaime CerdaJacobo

Publish Date: 2016/08/30

Volume: 18, Issue: 2, Pages: 123-147

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Abstract

In this paper we propose a genetic programming GP approach to the problem of prototype generation for nearestneighbor NN based classification The problem consists of learning a set of artificial instances that effectively represents the training set of a classification problem with the goal of reducing the storage requirements and the computational cost inherent in NN classifiers This work introduces an iterative GP technique to learn such artificial instances based on a nonlinear combination of instances available in the training set Experiments are reported in a benchmark for prototype generation Experimental results show our approach is very competitive with the state of the art in terms of accuracy and in its ability to reduce the training set size

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