SÁNCHEZ MONEDERO, JAVIER, Hervás-Martínez C. , Gutiérrez P.A. , CARBONERO RUZ, MARIANO, Moreno M.C.R. , Cruz-Ramírez M.
No
Neural Network World
Article
Científica
0.511
0.21
01/01/2010
2-s2.0-79952140374
Accuracy alone can be deceptive when evaluating the performance of a classifier, especially if the problem involves a high number of classes. This paper proposes an approach used for dealing with multi-class problems, which tries to avoid this issue. The approach is based on the Extreme Learning Machine (ELM) classifier, which is trained by using a Differential Evolution (DE) algorithm. Two error measures (Accuracy, C, and Sensitivity, S) are combined and applied as a fitness function for the algorithm. The proposed approach is able to obtain multi-class classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is evaluated over seven benchmark classification problems and one real problem, obtaining promising results. © ICS AS CR 2010.
Accuracy; Differential Evolution; Extreme learning machine; Multi objective; Multi-class classification; Sensitivity; Classification (of information); Classifiers; Evolutionary algorithms; Function evaluation; Learning systems; Multiobjective optimization; Neural networks