Title Evaluating the performance of evolutionary extreme learning machines by a combination of sensitivity and accuracy measures
Authors SÁNCHEZ MONEDERO, JAVIER, Hervás-Martínez C. , Gutiérrez P.A. , CARBONERO RUZ, MARIANO, Moreno M.C.R. , Cruz-Ramírez M.
External publication No
Means NEURAL NETW WORLD
Scope Article
Nature Científica
JCR Quartile 4
SJR Quartile 3
JCR Impact 0.511
SJR Impact 0.21
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952140374&partnerID=40&md5=ed38ea7072ac0d26510e0a87ed5e297c
Publication date 01/01/2010
Scopus Id 2-s2.0-79952140374
Abstract 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.
Keywords 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
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