Adjacent inputs with different labels and hardness in supervised learning
Corresponding author: Sebastián A. Grillo (sebastian.grillo@ua.edu.py)
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| 其他作者: | , , , , , |
| 格式: | article |
| 語言: | 英语 |
| 出版: |
2021
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| 主題: | |
| 在線閱讀: | https://doi.org/10.1109/ACCESS.2021.3131150 http://hdl.handle.net/20.500.14066/4519 |
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沒有標簽, 成為第一個標記此記錄!
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| _version_ | 1870612073263661056 |
|---|---|
| author | Grillo, Sebastián Alberto |
| author2 | Mello Román, Julio César Mello Román, Jorge Daniel Vázquez Noguera, José Luis García Torres, Miguel Divina, Federico Gardel Sotomayor, Pedro Esteban |
| author2_role | author author author author author author |
| author_browse | Divina, Federico García Torres, Miguel Gardel Sotomayor, Pedro Esteban Grillo, Sebastián Alberto Mello Román, Jorge Daniel Mello Román, Julio César Vázquez Noguera, José Luis |
| author_facet | Grillo, Sebastián Alberto Mello Román, Julio César Mello Román, Jorge Daniel Vázquez Noguera, José Luis García Torres, Miguel Divina, Federico Gardel Sotomayor, Pedro Esteban |
| author_role | author |
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| bitstream.url.fl_str_mv | http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/4/Adjacent%20inputs%20with%20different%20labels%20and%20hardness%20in%20supervised%20learning.jpg http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/1/Adjacent%20inputs%20with%20different%20labels%20and%20hardness%20in%20supervised%20learning.pdf http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/2/license_rdf http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/3/license.txt http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/5/Adjacent%20inputs%20with%20different%20labels%20and%20hardness%20in%20supervised%20learning.pdf.txt |
| dc.contributor.other.es.fl_str_mv | Universidad Americana/ INCADE S.A.E |
| dc.creator.none.fl_str_mv | Grillo, Sebastián Alberto Mello Román, Julio César Mello Román, Jorge Daniel Vázquez Noguera, José Luis García Torres, Miguel Divina, Federico Gardel Sotomayor, Pedro Esteban |
| dc.date.accessioned.none.fl_str_mv | 2025-02-03T17:14:44Z |
| dc.date.available.none.fl_str_mv | 2025-02-03T17:14:44Z |
| dc.date.issued.none.fl_str_mv | 2021-11-25 |
| dc.identifier.citation.en.fl_str_mv | Grillo, S. A., Mello Román, J. C., Mello Román, J. D., Vázquez Noguera, J. L., García Torres, M., & Divina, F. (2021). Adjacent inputs with different labels and hardness in supervised learning. IEEE Access, 9, 162487-162498. https://doi.org/10.1109/ACCESS.2021.3131150 |
| dc.identifier.doi.es.fl_str_mv | 10.1109/ACCESS.2021.3131150 |
| dc.identifier.essn.es.fl_str_mv | 2169-3536 |
| dc.identifier.other.es.fl_str_mv | https://doi.org/10.1109/ACCESS.2021.3131150 |
| dc.identifier.uri.none.fl_str_mv | http://hdl.handle.net/20.500.14066/4519 |
| dc.language.iso.es.fl_str_mv | eng |
| dc.publisher.es.fl_str_mv | Institute of Electrical and Electronics Engineers |
| dc.relation.projectCONACYT.es.fl_str_mv | PINV18-1199 |
| dc.rights.*.fl_str_mv | Atribución 4.0 Internacional |
| dc.rights.accessRights.es.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.rights.copyright.es.fl_str_mv | © 2021 Los Autores |
| dc.rights.uri.*.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.subject.classification.es.fl_str_mv | 7. Salud 7.3. Prevención, vigilancia y control de enfermedades transmisibles y no transmisibles |
| dc.subject.en.fl_str_mv | Complexity theory Data models Machine learning algorithms Measurement uncertainty Neural networks Particle measurements Supervised learning |
| dc.subject.ocde.es.fl_str_mv | 2. Ingeniería y Tecnología 2.2. Ingeniería Eléctrica, Electrónica e Informática [ingeniería eléctrica, electrónica, ingeniería y sistemas de comunicación, ingeniería informática (sólo equipos) y otras disciplinas afines] |
| dc.subject.other.es.fl_str_mv | Classification Data complexity Machine learning Overfitting Supervised learning |
| dc.title.es.fl_str_mv | Adjacent inputs with different labels and hardness in supervised learning |
| dc.type.es.fl_str_mv | info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Corresponding author: Sebastián A. Grillo (sebastian.grillo@ua.edu.py) |
| eu_rights_str_mv | openAccess |
| format | article |
| id | CONACYT_f070d1cf7f5e5af3cbbc027119fc7769 |
| identifier_str_mv | Grillo, S. A., Mello Román, J. C., Mello Román, J. D., Vázquez Noguera, J. L., García Torres, M., & Divina, F. (2021). Adjacent inputs with different labels and hardness in supervised learning. IEEE Access, 9, 162487-162498. https://doi.org/10.1109/ACCESS.2021.3131150 10.1109/ACCESS.2021.3131150 2169-3536 |
| language | eng |
| network_acronym_str | CONACYT |
| network_name_str | Repositorio Institucional CONACYT |
| oai_identifier_str | oai:repositorio.conacyt.gov.py:20.500.14066/4519 |
| publishDate | 2021 |
| publishDateSort | 2021 |
| repository.mail.fl_str_mv | repositorio.institucional@conacyt.gov.py |
| repository.name.fl_str_mv | Repositorio Institucional CONACYT |
| repository_id_str | |
| rights_invalid_str_mv | Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ © 2021 Los Autores |
| spelling | 57160011bddad9-f33b-43c9-8169-2cb6feaa29de60040eed708-3fa3-4ba3-b01d-29ebaa60cdb1600114460001af5a35-e2ad-4be2-8211-1ee4e7ab15cd60032a48873-af97-439e-bf26-eb8c2883849b600477c754f-d9b7-4bd1-a9f0-7cc92aec11f3600Universidad Americana/ INCADE S.A.E2025-02-03T17:14:44Z2025-02-03T17:14:44Z2021-11-25Grillo, S. A., Mello Román, J. C., Mello Román, J. D., Vázquez Noguera, J. L., García Torres, M., & Divina, F. (2021). Adjacent inputs with different labels and hardness in supervised learning. IEEE Access, 9, 162487-162498. https://doi.org/10.1109/ACCESS.2021.3131150https://doi.org/10.1109/ACCESS.2021.3131150http://hdl.handle.net/20.500.14066/451910.1109/ACCESS.2021.31311502169-3536Corresponding author: Sebastián A. Grillo (sebastian.grillo@ua.edu.py)An important aspect of the design of effective machine learning algorithms is the complexity analysis of classification problems. In this paper, we propose a study aimed at determining the relation between the number of adjacent inputs with different labels and the required number of examples for the task of inducing a classification model. To this aim, we first quantified the adjacent inputs with different labels as a property, using a measure denoted as Neighbour Input Variation (NIV). We analyzed the relation that NIV has to random data and overfitting. We then demonstrated that a threshold of NIV may determine if a classification model can generalize to unseen data. We also presented a case study aimed at analyzing threshold neural networks and the required first hidden layer size in function of NIV. Finally, we performed experiments with five popular algorithms analyzing the relation between NIV and the classification error on problems with few dimensions. We conclude that functions whose similar inputs have different outputs with high probability, considerably reduce the generalization capacity of classification algorithms.Consejo Nacional de Ciencia y TecnologíaPrograma Paraguayo para el Desarrollo de la Ciencia y Tecnología. Proyectos de investigación y desarrolloengInstitute of Electrical and Electronics EngineersAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccess© 2021 Los AutoresComplexity theoryData modelsMachine learning algorithmsMeasurement uncertaintyNeural networksParticle measurementsSupervised learning7. Salud7.3. Prevención, vigilancia y control de enfermedades transmisibles y no transmisiblesClassificationData complexityMachine learningOverfittingSupervised learning2. Ingeniería y Tecnología2.2. Ingeniería Eléctrica, Electrónica e Informática [ingeniería eléctrica, electrónica, ingeniería y sistemas de comunicación, ingeniería informática (sólo equipos) y otras disciplinas afines]Adjacent inputs with different labels and hardness in supervised learninginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionIEEE Access162487162498PINV18-11999Grillo, Sebastián AlbertoMello Román, Julio CésarMello Román, Jorge DanielVázquez Noguera, José LuisGarcía Torres, MiguelDivina, FedericoGardel Sotomayor, Pedro EstebanTHUMBNAILAdjacent inputs with different labels and hardness in supervised learning.jpgAdjacent inputs with different labels and hardness in supervised learning.jpgVista de primera página de artículo científicoimage/jpeg1109822http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/4/Adjacent%20inputs%20with%20different%20labels%20and%20hardness%20in%20supervised%20learning.jpg91605948b28e0b058521fdd8775aa060MD54ORIGINALAdjacent inputs with different labels and hardness in supervised learning.pdfAdjacent inputs with different labels and hardness in supervised learning.pdfArtículo científicoapplication/pdf1894441http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/1/Adjacent%20inputs%20with%20different%20labels%20and%20hardness%20in%20supervised%20learning.pdfbab9e8031f88f8e0e33175965c583037MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8908http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/2/license_rdf0175ea4a2d4caec4bbcc37e300941108MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81698http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/3/license.txt858b22fda432bd774e469302988c1974MD53TEXTAdjacent inputs with different labels and hardness in supervised learning.pdf.txtAdjacent inputs with different labels and hardness in supervised learning.pdf.txtExtracted texttext/plain49224http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4519/5/Adjacent%20inputs%20with%20different%20labels%20and%20hardness%20in%20supervised%20learning.pdf.txtcb05490dcbec20bab461d7a20711bab5MD5520.500.14066/4519oai:repositorio.conacyt.gov.py:20.500.14066/45192026-02-12 19:30:39.493Repositorio Institucional CONACYTrepositorio.institucional@conacyt.gov.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 |
| spellingShingle | Adjacent inputs with different labels and hardness in supervised learning Grillo, Sebastián Alberto Complexity theory Data models Machine learning algorithms Measurement uncertainty Neural networks Particle measurements Supervised learning 7. Salud 7.3. Prevención, vigilancia y control de enfermedades transmisibles y no transmisibles Classification Data complexity Machine learning Overfitting Supervised learning 2. Ingeniería y Tecnología 2.2. Ingeniería Eléctrica, Electrónica e Informática [ingeniería eléctrica, electrónica, ingeniería y sistemas de comunicación, ingeniería informática (sólo equipos) y otras disciplinas afines] |
| status_str | publishedVersion |
| title | Adjacent inputs with different labels and hardness in supervised learning |
| title_full | Adjacent inputs with different labels and hardness in supervised learning |
| title_fullStr | Adjacent inputs with different labels and hardness in supervised learning |
| title_full_unstemmed | Adjacent inputs with different labels and hardness in supervised learning |
| title_short | Adjacent inputs with different labels and hardness in supervised learning |
| title_sort | Adjacent inputs with different labels and hardness in supervised learning |
| topic | Complexity theory Data models Machine learning algorithms Measurement uncertainty Neural networks Particle measurements Supervised learning 7. Salud 7.3. Prevención, vigilancia y control de enfermedades transmisibles y no transmisibles Classification Data complexity Machine learning Overfitting Supervised learning 2. Ingeniería y Tecnología 2.2. Ingeniería Eléctrica, Electrónica e Informática [ingeniería eléctrica, electrónica, ingeniería y sistemas de comunicación, ingeniería informática (sólo equipos) y otras disciplinas afines] |
| url | https://doi.org/10.1109/ACCESS.2021.3131150 http://hdl.handle.net/20.500.14066/4519 |