Multi-objective Optimization Evolutionary Algorithms Applied to Paroxysmal Atrial Fibrillation Diagnosis Based on the k-Nearest Neighbours Classifier
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.
KeywordsEvolutionary Algorithm Multiobjective Optimization Pareto Optimal Solution Paroxysmal Atrial Fibrillation Strength Pareto Evolutionary Algorithm
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- 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
- 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
- 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
- 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
- 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 2002Google Scholar
- 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
- 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
- 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
- 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