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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/82/81

100.1.#.a: Miralvand, M.; Rasoolzadeh, S.; Majidi, M.

524.#.#.a: Miralvand, M., et al. (2015). Proposing a features preprocessing method based on artificial immune and minimum classification errors methods. Journal of Applied Research and Technology; Vol. 13 Núm. 4. Recuperado de https://repositorio.unam.mx/contenidos/45831

245.1.0.a: Proposing a features preprocessing method based on artificial immune and minimum classification errors methods

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2015

264.#.1.c: 2015-08-01

653.#.#.a: Artificial immune systems; Evolutionary algorithm; Optimization problem

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/82

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041.#.7.h: eng

520.3.#.a: Artificial immune systems that have been inspired from organic immune systems, have drawn many attentions in recent years (and have been considered) as an evolutionary algorithm, and have been applied in different papers. This algorithm can be used in two different areas of optimization and classification. In this paper, an artificial immune algorithm has been applied in optimization problem. In particular, artificial immune systems have been used for computing the mapping matrices and improving features. Comparison of results of proposed method with other preprocessing methods shows the superiority of the proposed method so that in 90% of cases it has the best performance based on different measures. Evaluation measures are including classification rate, variance and compression measure. All Rights Reserved © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.

773.1.#.t: Journal of Applied Research and Technology; Vol. 13 Núm. 4

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.1016/j.jart.2015.09.005

harvesting_date: 2023-11-08 13:10:00.0

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last_modified: 2024-03-19 14:00:00

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Artículo

Proposing a features preprocessing method based on artificial immune and minimum classification errors methods

Miralvand, M.; Rasoolzadeh, S.; Majidi, M.

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Miralvand, M., et al. (2015). Proposing a features preprocessing method based on artificial immune and minimum classification errors methods. Journal of Applied Research and Technology; Vol. 13 Núm. 4. Recuperado de https://repositorio.unam.mx/contenidos/45831

Descripción del recurso

Autor(es)
Miralvand, M.; Rasoolzadeh, S.; Majidi, M.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Proposing a features preprocessing method based on artificial immune and minimum classification errors methods
Fecha
2015-08-01
Resumen
Artificial immune systems that have been inspired from organic immune systems, have drawn many attentions in recent years (and have been considered) as an evolutionary algorithm, and have been applied in different papers. This algorithm can be used in two different areas of optimization and classification. In this paper, an artificial immune algorithm has been applied in optimization problem. In particular, artificial immune systems have been used for computing the mapping matrices and improving features. Comparison of results of proposed method with other preprocessing methods shows the superiority of the proposed method so that in 90% of cases it has the best performance based on different measures. Evaluation measures are including classification rate, variance and compression measure. All Rights Reserved © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.
Tema
Artificial immune systems; Evolutionary algorithm; Optimization problem
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces