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TNN
2010

Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks

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Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) reposito...
Juan Carlos Fernández Caballero, Francisco
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TNN
Authors Juan Carlos Fernández Caballero, Francisco José Martínez, César Hervás, Pedro Antonio Gutiérrez
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