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) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.
Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain. jcfernandez@uco.es
Juan Carlos
JC
Fernández Caballero
Francisco José
FJ
Martínez
César
C
Hervás
Pedro Antonio
PA
Gutiérrez
D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D003195:Computer Communication Networks / Q000706:statistics & numerical data; D003198:Computer Simulation; D006801:Humans; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D016571:Neural Networks, Computer; D012372:ROC Curve; D015203:Reproducibility of Results