Comparing Meta-heuristics for AdaBoost Training Applied to Platelets Detection

Abstract

This paper aims to compare the performance of different population-based meta-heuristics to train AdaBoost classifiers applied to detect platelets. AdaBoost classifiers are able to recognize complex patterns based on simple characteristics. We assessed three mono-objective techniques for AdaBoost training: Particle Swarm Optimization, Fish School Search and Genetic Algorithms. Our results show that the Genetic Algorithms outperformed the other two techniques for classifiers with just some few weak classifiers, while Particle Swarm Optimization achieved better results for classifiers with a higher number of weak classifiers, such as for twenty characteristics. We also tested two multi-objective optimizers, one based on Evolutionary Computation and another one based on Swarm Intelligence. The Multi-objective optimizers outperformed the mono-objective optimizers.

Algoritmo de Seleção de Classificadores para Reconhecimento de Padrões

“…Nascimento e Bastos-Filho também estenderam o reconhecimento para os outros elementos sanguíneos [20]. Mais recentemente, Bastos-Filho e Silva [21] realizaram um estudo comparativo entre técnicas monoobjetivas e multi-objetivas para efetuar o treinamento Ada-Boost e comparou seus resultados com o mesmo propósito: detectar plaquetas.…”