Skip to main content

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

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Carlos A. Coello Coello. An Updated Survey of GA-Based Multiobjective Optimization Techniques, Technical Report Lania-RD-98-08, Laboratorio Nacional de Informática Avanzada (LANIA), 1998.

    Google Scholar 

  2. Parks, G.T. and I. Miller.“Selective breeding in a multiobjective genetic algorithm”. In A.E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel (Editos).5th International Conference on Parallel Problem Solving from Nature (PPSN-V), Berlin, Germany, pp. 250–259. Springer.

    Google Scholar 

  3. Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Technical Report 70, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland, December 1999.

    Google Scholar 

  4. Laumans M., Zitzler E., Thiele L.: On the Effects of Archiving Elitism, and Density Based Selection in Evolutionary Multi-objective Optimization. 1st Conference on Evolutionary Multiobjective Optimization, pp 181–197. Springer-Verlag, 2001.

    Google Scholar 

  5. F. de Toro; J. Ortega.; J. Fernández; A.F. Díaz. PSFGA: A parallel Genetic Algorithm for Multiobjective Optimization. 10th Euromicro Workshop on Parallel and Distributed Processing. Gran Canaria, January 2002

    Google Scholar 

  6. F. de Toro, E Ros, S Mota, J Ortega: Non-invasive Atrial disease diagnosis using decision rules: A Mutiobjetive Optimization approach. 8th Iberoamerican Conference on Artificial Intelligence (IBERAMIA2002), November 2002, Sevilla, Spain.

    Google Scholar 

  7. Mota S., Ros E., Fernández F.J., Díaz A.F., Prieto, A.: ECG Parameter Characterization of Paroxysmal Atrial Fibrillation. 4th International Workshop on Biosignal Interpretation (BSI2002), 24th-26th June, 2002, Como, Italy.

    Google Scholar 

  8. Zitzler, E.; Thiele, L.: An Evolutionary algorithm for multiobjective optimization: The strength Pareto approach, Technical Report No. 43 (May 1998), Zürich: Computer Engineering and Networks Laboratory, Switzerland.

    Google Scholar 

  9. Ros E., Mota S., Toro F.J., Díaz A.F. and Fernández F.J.: Paroxysmal Atrial Fibrillation: Automatic Diagnosis Algorithm based on not fibrillating ECGs. 4th International Workshop on Biosignal Interpretation (BSI2002), 24th-26th June, 2002, Como, Italy.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

de Toro, F., Ros, E., Mota, S., Ortega, J. (2002). Multi-objective Optimization Evolutionary Algorithms Applied to Paroxysmal Atrial Fibrillation Diagnosis Based on the k-Nearest Neighbours Classifier. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_32

Download citation

  • DOI: https://doi.org/10.1007/3-540-36131-6_32

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00131-7

  • Online ISBN: 978-3-540-36131-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics