Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas
Esta tesis presenta un algoritmo innovador diseñado para realizar detección de anomalías en diversos conjuntos de datos. Este método, denominado Anomaly Detection through Density Matrices and Fourier Features (AD-DMKDE), integra estimación de densidad basada en kernels (en inglés Kernel Density Esti...
- Autores:
-
Bustos-Briñez, Oscar Alberto
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/85017
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Algoritmos (computadores)
Computer algorithms
Detección de anomalías
Algoritmos de aprendizaje automático
Estimación de densidad
Aprendizaje automático cuántico
Análisis de datos
Anomaly detection
Machine learning algorithms
Density estimation
Quantum machine learning
Data analysis
- Rights
- openAccess
- License
- Atribución-CompartirIgual 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas |
| dc.title.translated.eng.fl_str_mv |
Development of an anomaly detection algorithm based on kernel density estimation, density matrices and quantum measurement |
| title |
Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas |
| spellingShingle |
Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Algoritmos (computadores) Computer algorithms Detección de anomalías Algoritmos de aprendizaje automático Estimación de densidad Aprendizaje automático cuántico Análisis de datos Anomaly detection Machine learning algorithms Density estimation Quantum machine learning Data analysis |
| title_short |
Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas |
| title_full |
Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas |
| title_fullStr |
Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas |
| title_full_unstemmed |
Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas |
| title_sort |
Desarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticas |
| dc.creator.fl_str_mv |
Bustos-Briñez, Oscar Alberto |
| dc.contributor.advisor.none.fl_str_mv |
González Osorio, Fabio Augusto Gallego Mejia, Joseph Alejandro |
| dc.contributor.author.none.fl_str_mv |
Bustos-Briñez, Oscar Alberto |
| dc.contributor.researchgroup.spa.fl_str_mv |
Mindlab |
| dc.contributor.orcid.spa.fl_str_mv |
0000-0003-0704-9117 |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
| topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Algoritmos (computadores) Computer algorithms Detección de anomalías Algoritmos de aprendizaje automático Estimación de densidad Aprendizaje automático cuántico Análisis de datos Anomaly detection Machine learning algorithms Density estimation Quantum machine learning Data analysis |
| dc.subject.lemb.spa.fl_str_mv |
Algoritmos (computadores) |
| dc.subject.lemb.eng.fl_str_mv |
Computer algorithms |
| dc.subject.proposal.spa.fl_str_mv |
Detección de anomalías Algoritmos de aprendizaje automático Estimación de densidad Aprendizaje automático cuántico Análisis de datos |
| dc.subject.proposal.eng.fl_str_mv |
Anomaly detection Machine learning algorithms Density estimation Quantum machine learning Data analysis |
| description |
Esta tesis presenta un algoritmo innovador diseñado para realizar detección de anomalías en diversos conjuntos de datos. Este método, denominado Anomaly Detection through Density Matrices and Fourier Features (AD-DMKDE), integra estimación de densidad basada en kernels (en inglés Kernel Density Estimation o KDE) y aprendizaje de máquina (conocida como Machine Learning en inglés) con las matrices de densidad y la medición cuántica, dos prometedores conceptos provenientes del campo de la computación cuántica. Se establecen las bases teóricas y metodológicas que sustentan este método; asimismo, se presentan los detalles de su desarrollo e implementación. Se realiza una comparación sistemática del algoritmo propuesto contra doce métodos variados de detección de anomalías; AD-DMKDE muestra un rendimiento competitivo al ser aplicado sobre una selección de veinticuatro conjuntos de datos. Se establecen las fortalezas y limitaciones del algoritmo propuesto y, a partir del análisis estadístico de su rendimiento, se enuncian una serie de conclusiones y posibles líneas de trabajo futuro. (Texto tomado d la fuente) |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-11-29T14:42:39Z |
| dc.date.available.none.fl_str_mv |
2023-11-29T14:42:39Z |
| dc.date.issued.none.fl_str_mv |
2023 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/85017 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/85017 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
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Atribución-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80600Gallego Mejia, Joseph Alejandroefefb1c0f60204dcaa3f129555307e90600Bustos-Briñez, Oscar Alberto9798ee4fc7d48e13a2f48b78d8374940600Mindlab0000-0003-0704-91172023-11-29T14:42:39Z2023-11-29T14:42:39Z2023https://repositorio.unal.edu.co/handle/unal/85017Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Esta tesis presenta un algoritmo innovador diseñado para realizar detección de anomalías en diversos conjuntos de datos. Este método, denominado Anomaly Detection through Density Matrices and Fourier Features (AD-DMKDE), integra estimación de densidad basada en kernels (en inglés Kernel Density Estimation o KDE) y aprendizaje de máquina (conocida como Machine Learning en inglés) con las matrices de densidad y la medición cuántica, dos prometedores conceptos provenientes del campo de la computación cuántica. Se establecen las bases teóricas y metodológicas que sustentan este método; asimismo, se presentan los detalles de su desarrollo e implementación. Se realiza una comparación sistemática del algoritmo propuesto contra doce métodos variados de detección de anomalías; AD-DMKDE muestra un rendimiento competitivo al ser aplicado sobre una selección de veinticuatro conjuntos de datos. Se establecen las fortalezas y limitaciones del algoritmo propuesto y, a partir del análisis estadístico de su rendimiento, se enuncian una serie de conclusiones y posibles líneas de trabajo futuro. (Texto tomado d la fuente)This thesis presents a novel algorithm designed to perform anomaly detection on multiple data sets. This method, called Anomaly Detection through Density Matrices and Fourier Features (AD-DMKDE), integrates Kernel Density Estimation (KDE) and Machine Learning with density matrices and quantum measurement, two promising concepts from quantum computing. The theoretical and methodological foundations that support this method are established, along with the details of its development and implementation. A systematic comparison of the proposed algorithm with twelve state-of-the-art anomaly detection methods is presented, and AD-DMKDE demonstrates competitive performance when applied on twenty-four benchmark data sets. The strengths and limitations of the proposed algorithm are identified, and based on a statistical analysis of its performance, a series of conclusions and possible lines of future work are stated.MaestríaComputación Teóricaxiv, 52 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresAlgoritmos (computadores)Computer algorithmsDetección de anomalíasAlgoritmos de aprendizaje automáticoEstimación de densidadAprendizaje automático cuánticoAnálisis de datosAnomaly detectionMachine learning algorithmsDensity estimationQuantum machine learningData analysisDesarrollo de un algoritmo para detección de anomalías con base en estimación de densidad basada en kernels, matrices de densidad y medidas cuánticasDevelopment of an anomaly detection algorithm based on kernel density estimation, density matrices and quantum measurementTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAggarwal, Charu C. ; Aggarwal, Charu C.: An introduction to outlier analysis. 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En: Applied Sciences 12 (2022), Nr. 11, p. 5336BibliotecariosEstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85017/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1012433384.2023.pdf1012433384.2023.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf844836https://repositorio.unal.edu.co/bitstream/unal/85017/2/1012433384.2023.pdf38b29cce6e13ee48de10b46f1edc7c53MD52THUMBNAIL1012433384.2023.pdf.jpg1012433384.2023.pdf.jpgGenerated Thumbnailimage/jpeg4506https://repositorio.unal.edu.co/bitstream/unal/85017/3/1012433384.2023.pdf.jpg1afc1ae3d6f38ff54ad3617bf1010c26MD53unal/85017oai:repositorio.unal.edu.co:unal/850172024-08-19 23:11:20.943Repositorio Institucional Universidad Nacional de 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