Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section
Extreme learning machine is a neural network algorithm widely accepted in the scientific community due to the simplicity of the model and its good results in classification and regression problems; digital image processing, medical diagnosis, and signal recognition are some applications in the field...
- Autores:
-
Gelvez-Almeida, E
Mora, M
Huérfano-Maldonado, Y
Salazar-Jurado, E
Martínez-Jeraldo, N
Lozada-Yavina, R
Baldera-Moreno, Y
Tobar, L
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/13163
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/13163
https://doi.org/10.1088/1742-6596/2515/1/012003
- Palabra clave:
- Extreme Learning Machine
Classification and regression problems
Digital image processing
Neural networks
Simulated
Sequential method
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
id |
USIMONBOL2_49602cdadfb737aa57a51fa0ce6494c7 |
---|---|
oai_identifier_str |
oai:bonga.unisimon.edu.co:20.500.12442/13163 |
network_acronym_str |
USIMONBOL2 |
network_name_str |
Repositorio Digital USB |
repository_id_str |
|
dc.title.eng.fl_str_mv |
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section |
title |
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section |
spellingShingle |
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section Extreme Learning Machine Classification and regression problems Digital image processing Neural networks Simulated Sequential method |
title_short |
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section |
title_full |
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section |
title_fullStr |
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section |
title_full_unstemmed |
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section |
title_sort |
Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section |
dc.creator.fl_str_mv |
Gelvez-Almeida, E Mora, M Huérfano-Maldonado, Y Salazar-Jurado, E Martínez-Jeraldo, N Lozada-Yavina, R Baldera-Moreno, Y Tobar, L |
dc.contributor.author.none.fl_str_mv |
Gelvez-Almeida, E Mora, M Huérfano-Maldonado, Y Salazar-Jurado, E Martínez-Jeraldo, N Lozada-Yavina, R Baldera-Moreno, Y Tobar, L |
dc.subject.eng.fl_str_mv |
Extreme Learning Machine Classification and regression problems Digital image processing Neural networks Simulated Sequential method |
topic |
Extreme Learning Machine Classification and regression problems Digital image processing Neural networks Simulated Sequential method |
description |
Extreme learning machine is a neural network algorithm widely accepted in the scientific community due to the simplicity of the model and its good results in classification and regression problems; digital image processing, medical diagnosis, and signal recognition are some applications in the field of physics addressed with these neural networks. The algorithm must be executed with an adequate number of neurons in the hidden layer to obtain good results. Identifying the appropriate number of neurons in the hidden layer is an open problem in the extreme learning machine field. The search process has a high computational cost if carried out sequentially, given the complexity of the calculations as the number of neurons increases. In this work, we use the search of the golden section and simulated annealing as heuristic methods to calculate the appropriate number of neurons in the hidden layer of an Extreme Learning Machine; for the experiments, three real databases were used for the classification problem and a synthetic database for the regression problem. The results show that the search for the appropriate number of neurons is accelerated up to 4.5× times with simulated annealing and up to 95.7× times with the golden section search compared to a sequential method in the highest-dimensional database. |
publishDate |
2023 |
dc.date.accessioned.none.fl_str_mv |
2023-08-18T18:12:03Z |
dc.date.available.none.fl_str_mv |
2023-08-18T18:12:03Z |
dc.date.issued.none.fl_str_mv |
2023 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.driver.eng.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.spa.spa.fl_str_mv |
Artículo científico |
dc.identifier.issn.none.fl_str_mv |
17426596 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12442/13163 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1088/1742-6596/2515/1/012003 |
identifier_str_mv |
17426596 |
url |
https://hdl.handle.net/20.500.12442/13163 https://doi.org/10.1088/1742-6596/2515/1/012003 |
dc.language.iso.eng.fl_str_mv |
eng |
language |
eng |
dc.rights.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights.accessrights.eng.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.mimetype.eng.fl_str_mv |
pdf |
dc.publisher.eng.fl_str_mv |
IOP Publishing |
dc.source.eng.fl_str_mv |
Journal of Physics: Conference Series |
dc.source.none.fl_str_mv |
Vol. 2515 (2023) |
institution |
Universidad Simón Bolívar |
bitstream.url.fl_str_mv |
https://bonga.unisimon.edu.co/bitstreams/5290e7a2-f7d5-48e2-b388-d884bbc309f2/download https://bonga.unisimon.edu.co/bitstreams/e8627638-4568-419c-8958-760207b26f7d/download https://bonga.unisimon.edu.co/bitstreams/dcfaf395-ad4a-4181-b5b0-504f5c1d2336/download https://bonga.unisimon.edu.co/bitstreams/94810abd-36b2-4bf6-a9d4-d12f8600425b/download https://bonga.unisimon.edu.co/bitstreams/50174682-37f6-4ebf-a0f8-9e5ad7bb033e/download https://bonga.unisimon.edu.co/bitstreams/a8a2d1d1-2a06-4977-bbc7-77adba97e136/download https://bonga.unisimon.edu.co/bitstreams/f478e2f1-d492-40d0-a2bf-bf0934f8992c/download |
bitstream.checksum.fl_str_mv |
0e265e1ab8d684e25ae017602fe791af 4460e5956bc1d1639be9ae6146a50347 733bec43a0bf5ade4d97db708e29b185 9b268ed50b1b23970c760b93c7ef0ad2 9b268ed50b1b23970c760b93c7ef0ad2 488bda6547cd0eeddaa16fd8276d374e 488bda6547cd0eeddaa16fd8276d374e |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Digital Universidad Simón Bolívar |
repository.mail.fl_str_mv |
repositorio.digital@unisimon.edu.co |
_version_ |
1814076125776707584 |
spelling |
Gelvez-Almeida, E55062614-d175-4da1-834a-d7e54dcc92deMora, M630644e5-97cc-482b-b5d0-8f65851e3399Huérfano-Maldonado, Ya3edcb9b-120d-45b6-a7f2-d56e9fa05811Salazar-Jurado, E4ae59707-3b9a-443a-aa58-da4e56fd6045Martínez-Jeraldo, Nd919f4f5-afc4-4dbb-8e89-cc194943833eLozada-Yavina, Rf9fc8fce-65bd-42df-b52a-b28b424d69ddBaldera-Moreno, Yacfe1c46-3708-4d52-a3e8-bf9bea2b3f9cTobar, L71c05ad6-4202-4eba-8c54-ca5c3b0a91652023-08-18T18:12:03Z2023-08-18T18:12:03Z202317426596https://hdl.handle.net/20.500.12442/13163https://doi.org/10.1088/1742-6596/2515/1/012003Extreme learning machine is a neural network algorithm widely accepted in the scientific community due to the simplicity of the model and its good results in classification and regression problems; digital image processing, medical diagnosis, and signal recognition are some applications in the field of physics addressed with these neural networks. The algorithm must be executed with an adequate number of neurons in the hidden layer to obtain good results. Identifying the appropriate number of neurons in the hidden layer is an open problem in the extreme learning machine field. The search process has a high computational cost if carried out sequentially, given the complexity of the calculations as the number of neurons increases. In this work, we use the search of the golden section and simulated annealing as heuristic methods to calculate the appropriate number of neurons in the hidden layer of an Extreme Learning Machine; for the experiments, three real databases were used for the classification problem and a synthetic database for the regression problem. The results show that the search for the appropriate number of neurons is accelerated up to 4.5× times with simulated annealing and up to 95.7× times with the golden section search compared to a sequential method in the highest-dimensional database.pdfengIOP PublishingAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesVol. 2515 (2023)Extreme Learning MachineClassification and regression problemsDigital image processingNeural networksSimulatedSequential methodEstimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden sectioninfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C and Lendasse A 2009 OP-ELM: optimally pruned extreme learning machine IEEE Transactions on Neural Networks 21(1) 158Miche Y, Van Heeswijk M, Bas P, Simula O and Lendasse A 2011 TROP-ELM: a double-regularized elm using lars and tikhonov regularization Neurocomputing 74(16) 2413Soria-Olivas E, Gomez-Sanchis J, Martin J D, Vila-Frances J, Martinez M, Magdalena J R and Serrano A J 2011 BELM: bayesian extreme learning machine IEEE Transactions on Neural Networks 22(3) 505Thornton C, Hutter F, Hoos H H and Leyton-Brown K 2013 Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ed Ghani R, Senator T E and Bradley P (Chicago: Association for Computing Machinery) p 847Ahila R, Sadasivam V and Manimala K 2015 An integrated PSO for parameter determination and feature selection of elm and its application in classification of power system disturbances Applied Soft Computing 32 23Ali M H, Fadlizolkipi M, Firdaus A and Khidzir N Z 2018 A hybrid particle swarm optimization-extreme learning machine approach for intrusion detection system Proceedings of the 2018 IEEE Student Conference on Research and Development (SCOReD) (Selangor: IEEE) p 1Bao X, Li Y, Li J, Shi R and Ding X 2021 Prediction of train arrival delay using hybrid ELM-PSO approach Journal of Advanced Transportation 2021 7763125Cao Z, Xia J, Zhang M, Jin J, Deng L, Wang X and Qu J 2015 Optimization of gear blank preforms based on a new R-GPLVM model utilizing GA-ELM Knowledge-Based Systems 83 66Ahmad W, Ayub N, Ali T, Irfan M, Awais M, Shiraz M and Glowacz A 2020 Towards short term electricity load forecasting using improved support vector machine and extreme learning machine Energies 13(11) 2907Martinho A D, Ribeiro C, Gorodetskaya Y, Fonseca T L and Goliatt L 2020 Extreme learning machine with evolutionary parameter tuning applied to forecast the daily natural flow at cahora bassa dam, mozambique Bioinspired Methods and Their Applications ed Filipiˇc B, Minisci E and Vasile M (Cham: Springer) p 255Kirkpatrick S, Gelatt C D and Vecchi M P 1983 Optimization by simulated annealing Science 220(4598) 671Koupaei J A, Hosseini S M M and Ghaini F M 2016 A new optimization algorithm based on chaotic maps and golden section search method Engineering Applications of Artificial Intelligence 50 201Lyche T 2020 Numerical Linear Algebra and Matrix Factorizations vol 22 (Oslo: Springer)Huang G B, Zhu Q Y and Siew C K 2006 Extreme learning machine: theory and applications Neurocomputing 70(1-3) 489Haznedar B, Arslan M T and Kalinli A 2021 Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data Medical & Biological Engineering & Computing 59(3) 497Kubat M 1999 Neural networks: a comprehensive foundation The Knowledge Engineering Review vol 13 ed Simon H (New York: Cambridge University Press) p 409Peltonen J, Klami A and Kaski S 2004 Improved learning of riemannian metrics for exploratory analysis Neural Networks 17(8-9) 1087Hsu C W and Lin C J 2002 A comparison of methods for multiclass support vector machines IEEE Transactions on Neural Networks 13(2) 415Zhang M, Wu Q, Xu Z et al. 2019 Tuning extreme learning machine by an improved electromagnetism-like mechanism algorithm for classification problem Mathematical Biosciences and Engineering 16(5) 4692ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf693372https://bonga.unisimon.edu.co/bitstreams/5290e7a2-f7d5-48e2-b388-d884bbc309f2/download0e265e1ab8d684e25ae017602fe791afMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/e8627638-4568-419c-8958-760207b26f7d/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/dcfaf395-ad4a-4181-b5b0-504f5c1d2336/download733bec43a0bf5ade4d97db708e29b185MD53TEXT10_2023_ART_Estimation of the optimal.pdf.txt10_2023_ART_Estimation of the optimal.pdf.txtExtracted texttext/plain24982https://bonga.unisimon.edu.co/bitstreams/94810abd-36b2-4bf6-a9d4-d12f8600425b/download9b268ed50b1b23970c760b93c7ef0ad2MD54PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain24982https://bonga.unisimon.edu.co/bitstreams/50174682-37f6-4ebf-a0f8-9e5ad7bb033e/download9b268ed50b1b23970c760b93c7ef0ad2MD56THUMBNAIL10_2023_ART_Estimation of the optimal.pdf.jpg10_2023_ART_Estimation of the optimal.pdf.jpgGenerated Thumbnailimage/jpeg4235https://bonga.unisimon.edu.co/bitstreams/a8a2d1d1-2a06-4977-bbc7-77adba97e136/download488bda6547cd0eeddaa16fd8276d374eMD55PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg4235https://bonga.unisimon.edu.co/bitstreams/f478e2f1-d492-40d0-a2bf-bf0934f8992c/download488bda6547cd0eeddaa16fd8276d374eMD5720.500.12442/13163oai:bonga.unisimon.edu.co:20.500.12442/131632024-08-14 21:53:09.577http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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 |