A new artificial immune system based on continuous learning for pattern recognition

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dc.contributor.authorSouza, Simone S. F.
dc.contributor.authorLima, Fernando P. A.
dc.contributor.authorChavarette, Fábio R. [UNESP]
dc.contributor.institutionState University of Mato Grosso (UNEMAT)
dc.contributor.institutionAdvanced Campus of Tangará da Serra
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2021-06-25T10:49:27Z
dc.date.available2021-06-25T10:49:27Z
dc.date.issued2020-01-01
dc.description.abstractThis paper presents a novel approach for pattern recognition based on continuous training inspired by the biological immune system operation. The main objective of this paper is to present a method capable of continually learn, i.e., being able to address new types of patterns without the need to restart the training process (artificial immune system with incremental learning). It is a useful method for solving problems involving a permanent knowledge extraction, e.g., 3D facial expression recognition, whose quality of the solutions is strongly dependent on a continuous training process. In this context, two artificial immune algorithms are employed: (1) the negative selection algorithm, which is responsible for the pattern recognition process and (2) the clonal selection algorithm, which is responsible for the learning process. The main application of this method is in assisting in decision-making on problems related to pattern recognition process. To evaluate and validate the efficiency of this method, the system has been tested on handwritten character recognition, which is a classic problem in the literature. The results show efficiency, accuracy and robustness of the proposed methodology.en
dc.description.affiliationState University of Mato Grosso (UNEMAT), Campus of Tangará da Serra, Rodovia MT-358, Km 07, Jardim Aeroporto
dc.description.affiliationFederal Institute of Science and Technology Education of Mato Grosso (IFMT) Advanced Campus of Tangará da Serra, Rua 28, 980 N, Vila Horizonte
dc.description.affiliationMathematical Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Universidade Estadual Paulista Júlio de Mesquita Filho, Av. Brasil, 56, PO Box 31
dc.description.affiliationUnespMathematical Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Universidade Estadual Paulista Júlio de Mesquita Filho, Av. Brasil, 56, PO Box 31
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2019/10515-4
dc.description.sponsorshipIdCNPq: 312972/2019-9
dc.format.extent34-44
dc.identifierhttp://dx.doi.org/10.22456/2175-2745.102061
dc.identifier.citationRevista de Informatica Teorica e Aplicada, v. 27, n. 4, p. 34-44, 2020.
dc.identifier.doi10.22456/2175-2745.102061
dc.identifier.issn2175-2745
dc.identifier.issn0103-4308
dc.identifier.scopus2-s2.0-85099306102
dc.identifier.urihttp://hdl.handle.net/11449/207131
dc.language.isoeng
dc.relation.ispartofRevista de Informatica Teorica e Aplicada
dc.sourceScopus
dc.subjectArtificial Immune Systems
dc.subjectClonal Selection Algorithm
dc.subjectContinuous Learning
dc.subjectNegative Selection Algorithm
dc.subjectPattern Recognition
dc.titleA new artificial immune system based on continuous learning for pattern recognitionen
dc.titleUm novo sistema imunológico artificial baseado no aprendizado contínuo para reconhecimento de padrõespt
dc.typeArtigo

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