Projeto de classificadores de padrões baseados em protótipos usando evolução diferencial

Abstract

In this Master's dissertation we introduce an evolutionary approach for the eficient design of prototyp e-based classi ers using di erential evolution (DE). For this purp ose we amalgamate ideas from the Learning Vector Quantization (LVQ) framework for sup ervised classi cation by Kohonen (KOHONEN, 2001), with the DE-based automatic clustering approach by Das et al. (DAS; ABRAHAM; KONAR, 2008) in order to evolve sup ervised classi ers. The prop osed approach is able to determine b oth the optimal numb er of prototyp es p er class and the corresp onding p ositions of these prototyp es in the data space. By means of comprehensive computer simulations on b enchmarking datasets, we show that the resulting classi er, named LVQ-DE, consistently outp erforms state-of-the-art prototyp e-based classi ers