Neural network ensembles: Immune-inspired approaches to the diversity of components

dc.contributor.authorPasti R.
dc.contributor.authorDe Castro L.N.
dc.contributor.authorCoelho G.P.
dc.contributor.authorVon Zuben F.J.
dc.date.accessioned2024-03-13T01:31:03Z
dc.date.available2024-03-13T01:31:03Z
dc.date.issued2010
dc.description.abstractThis work applies two immune-inspired algorithms, namely opt-aiNet and omni-aiNet, to train multi-layer perceptrons (MLPs) to be used in the construction of ensembles of classifiers. The main goal is to investigate the influence of the diversity of the set of solutions generated by each of these algorithms, and if these solutions lead to improvements in performance when combined in ensembles. omni-aiNet is a multi-objective optimization algorithm and, thus, explicitly maximizes the components' diversity at the same time it minimizes their output errors. The opt-aiNet algorithm, by contrast, was originally designed to solve single-objective optimization problems, focusing on the minimization of the output error of the classifiers. However, an implicit diversity maintenance mechanism stimulates the generation of MLPs with different weights, which may result in diverse classifiers. The performances of opt-aiNet and omni-aiNet are compared with each other and with that of a second-order gradient-based algorithm, named MSCG. The results obtained show how the different diversity maintenance mechanisms presented by each algorithm influence the gain in performance obtained with the use of ensembles. © 2009 Springer Science+Business Media B.V.
dc.description.firstpage625
dc.description.issuenumber3
dc.description.lastpage653
dc.description.volume9
dc.identifier.doi10.1007/s11047-009-9124-1
dc.identifier.issn1567-7818
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/37158
dc.relation.ispartofNatural Computing
dc.rightsAcesso Restrito
dc.subject.otherlanguageArtificial immune systems
dc.subject.otherlanguageDiversity of components
dc.subject.otherlanguageEnsembles of classifiers
dc.subject.otherlanguageMulti-layer perceptrons
dc.subject.otherlanguageMulti-objective optimization
dc.titleNeural network ensembles: Immune-inspired approaches to the diversity of components
dc.typeArtigo de revisão
local.scopus.citations8
local.scopus.eid2-s2.0-77955925117
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77955925117&origin=inward
Arquivos