On the efficient design of a prototype-based classifier using differential evolution

In this paper we introduce an evolutionary approach for the efficient design of prototype-based classifiers using differential evolution (DE). For this purpose we amalgamate ideas from the Learning Vector Quantization (LVQ) framework for supervised classification by Kohonen [1], [2], with the DEbased automatic clustering approach by Das et al. [3] in order to evolve supervised classifiers. The proposed approach is able to determine both the optimal number of prototypes per class and the corresponding positions of these prototypes in the data space. By means of comprehensive computer simulations on benchmarking datasets, we show that the resulting classifier, named LVQ-DE, consistently outperforms state-of-the-art prototype-based classifiers.