Evolutionary Adaptive Self-Generating Prototypes for imbalanced datasets

The nearest neighbor (NN) is one of the most well known classifiers in pattern recognition. Despite the high classification accuracy, the NN has several drawbacks: high storage requirements, bad time of response, and high noise sensitivity. Prototype Generation (PG) is one of the most well-known solutions to tackle these shortcomings. In supervised classification, many real world datasets do not have an equitable distribution among the different classes, these are called imbalanced datasets. Many PG techniques that have a high classification accuracy in regular datasets, have a poor performance when dealing with imbalanced datasets. The Self-Generating Prototypes (SGP) is one of these techniques. The Adaptive Self-Generating Prototypes was proposed to tackle the SGP problem with imbalanced datasets, but, in doing so, the reduction rate is compromised. This paper proposes the Evolutionary Adaptive Self-Generating Prototypes (EASGP), a SGP based technique with iterative merging and evolutionary pruning to help find the optimal solution. An experimental analysis is performed with datasets of different levels of imbalance ratio and statistical tests are used to evaluate the proposed technique. The results obtained show that EASGP outperforms previous SGP based algorithms in classification accuracy and reduction.