An iterative genetic programming approach to prototype generation

In this paper, we propose a genetic programming (GP) approach to the problem of prototype generation for nearest-neighbor (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 non-linear 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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