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