A new artificial immune system based on continuous learning for pattern recognition
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.22456/2175-2745.102061 http://hdl.handle.net/11449/207131 |
Resumo: | This 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. |
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Repositório Institucional da UNESP |
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A new artificial immune system based on continuous learning for pattern recognitionUm novo sistema imunológico artificial baseado no aprendizado contínuo para reconhecimento de padrõesArtificial Immune SystemsClonal Selection AlgorithmContinuous LearningNegative Selection AlgorithmPattern RecognitionThis 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)State University of Mato Grosso (UNEMAT), Campus of Tangará da Serra, Rodovia MT-358, Km 07, Jardim AeroportoFederal Institute of Science and Technology Education of Mato Grosso (IFMT) Advanced Campus of Tangará da Serra, Rua 28, 980 N, Vila HorizonteMathematical Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Universidade Estadual Paulista Júlio de Mesquita Filho, Av. Brasil, 56, PO Box 31Mathematical Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Universidade Estadual Paulista Júlio de Mesquita Filho, Av. Brasil, 56, PO Box 31FAPESP: 2019/10515-4CNPq: 312972/2019-9State University of Mato Grosso (UNEMAT)Advanced Campus of Tangará da SerraUniversidade Estadual Paulista (Unesp)Souza, Simone S. F.Lima, Fernando P. A.Chavarette, Fábio R. [UNESP]2021-06-25T10:49:27Z2021-06-25T10:49:27Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article34-44http://dx.doi.org/10.22456/2175-2745.102061Revista de Informatica Teorica e Aplicada, v. 27, n. 4, p. 34-44, 2020.2175-27450103-4308http://hdl.handle.net/11449/20713110.22456/2175-2745.1020612-s2.0-85099306102Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRevista de Informatica Teorica e Aplicadainfo:eu-repo/semantics/openAccess2024-07-10T15:41:40Zoai:repositorio.unesp.br:11449/207131Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:49:59.696485Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A new artificial immune system based on continuous learning for pattern recognition Um novo sistema imunológico artificial baseado no aprendizado contínuo para reconhecimento de padrões |
title |
A new artificial immune system based on continuous learning for pattern recognition |
spellingShingle |
A new artificial immune system based on continuous learning for pattern recognition Souza, Simone S. F. Artificial Immune Systems Clonal Selection Algorithm Continuous Learning Negative Selection Algorithm Pattern Recognition |
title_short |
A new artificial immune system based on continuous learning for pattern recognition |
title_full |
A new artificial immune system based on continuous learning for pattern recognition |
title_fullStr |
A new artificial immune system based on continuous learning for pattern recognition |
title_full_unstemmed |
A new artificial immune system based on continuous learning for pattern recognition |
title_sort |
A new artificial immune system based on continuous learning for pattern recognition |
author |
Souza, Simone S. F. |
author_facet |
Souza, Simone S. F. Lima, Fernando P. A. Chavarette, Fábio R. [UNESP] |
author_role |
author |
author2 |
Lima, Fernando P. A. Chavarette, Fábio R. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
State University of Mato Grosso (UNEMAT) Advanced Campus of Tangará da Serra Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Souza, Simone S. F. Lima, Fernando P. A. Chavarette, Fábio R. [UNESP] |
dc.subject.por.fl_str_mv |
Artificial Immune Systems Clonal Selection Algorithm Continuous Learning Negative Selection Algorithm Pattern Recognition |
topic |
Artificial Immune Systems Clonal Selection Algorithm Continuous Learning Negative Selection Algorithm Pattern Recognition |
description |
This 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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 2021-06-25T10:49:27Z 2021-06-25T10:49:27Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.22456/2175-2745.102061 Revista de Informatica Teorica e Aplicada, v. 27, n. 4, p. 34-44, 2020. 2175-2745 0103-4308 http://hdl.handle.net/11449/207131 10.22456/2175-2745.102061 2-s2.0-85099306102 |
url |
http://dx.doi.org/10.22456/2175-2745.102061 http://hdl.handle.net/11449/207131 |
identifier_str_mv |
Revista de Informatica Teorica e Aplicada, v. 27, n. 4, p. 34-44, 2020. 2175-2745 0103-4308 10.22456/2175-2745.102061 2-s2.0-85099306102 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Revista de Informatica Teorica e Aplicada |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
34-44 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128986624032768 |