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Multi-objective Optimization Evolutionary Algorithms Applied to Paroxysmal Atrial Fibrillation Diagnosis Based on the k-Nearest Neighbours Classifier

  • Francisco de Toro
  • Eduardo Ros
  • Sonia Mota
  • Julio Ortega
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2527)

Abstract

In this paper, multi-objective optimization is applied to determine the parameters for a k-nearest neighbours classifier that has been used in the diagnosis of Paroxysmal Atrial Fibrillation (PAF), in order to get optimal combinations of classification rate, sensibility and specificity. We have considered three different evolutionary algorithms for implementing the multiobjective optimization of parameters: the Single Front Genetic Algorithm (SFGA), an improved version of SFGA, called New Single Front Genetic Algorithm (NSFGA), and the Strength Pareto Evolutionary Algorithm (SPEA). The experimental results and the comparison of the different methods, done by using the hypervolume metric, show that multi-objective optimization constitutes an adequate alternative to combinatorial scanning techniques.

Keywords

Evolutionary Algorithm Multiobjective Optimization Pareto Optimal Solution Paroxysmal Atrial Fibrillation Strength Pareto Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Francisco de Toro
    • 1
  • Eduardo Ros
    • 2
  • Sonia Mota
    • 2
  • Julio Ortega
    • 2
  1. 1.Departamento de Ingeniería Electrónica, Sistemas Informáticos y AutomáticaUniversidad de HuelvaSpain
  2. 2.Departamento de Arquitectura y Tecnología de ComputadoresUniversidad de GranadaSpain

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