Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction

This thesis explores aspects in the field of machine learning, and specifically of pattern classification and regression or function approximation. Although there are many methods of classification for multi-dimensional patterns, in general, they all behave like "black boxes" where the exp...

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Detalhes bibliográficos
Autor: Alawadi, Sadi
Formato: tesis doctoral
Fecha de publicación:2018
País:España
Recursos:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/16633
Acesso em linha:http://hdl.handle.net/10347/16633
Access Level:acceso abierto
Palavra-chave:Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330412 Dispositivos de control
Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330406 Arquitectura de ordenadores
Materias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120304 Inteligencia artificial
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oai_identifier_str oai:minerva.usc.gal:10347/16633
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction
title Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction
spellingShingle Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction
Alawadi, Sadi
Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330412 Dispositivos de control
Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330406 Arquitectura de ordenadores
Materias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120304 Inteligencia artificial
title_short Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction
title_full Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction
title_fullStr Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction
title_full_unstemmed Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction
title_sort Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction
dc.creator.none.fl_str_mv Alawadi, Sadi
author Alawadi, Sadi
author_facet Alawadi, Sadi
author_role author
dc.contributor.none.fl_str_mv Fernández Delgado, Manuel
Mera Pérez, David
Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
Escola Técnica Superior de Enxeñaría
Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS)
dc.subject.none.fl_str_mv Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330412 Dispositivos de control
Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330406 Arquitectura de ordenadores
Materias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120304 Inteligencia artificial
topic Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330412 Dispositivos de control
Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330406 Arquitectura de ordenadores
Materias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120304 Inteligencia artificial
description This thesis explores aspects in the field of machine learning, and specifically of pattern classification and regression or function approximation. Although there are many methods of classification for multi-dimensional patterns, in general, they all behave like "black boxes" where the explanation of their operation is difficult or impossible. This thesis develops methods of reducing the dimensionality of data to project multi-dimensional classification problems over a two-dimensional space (a plane). The classifiers can thus be used to learn the projected data and to create two-dimensional maps of classification problems whose graphic nature makes intuitive and easy to understand, helping to explain the classification problem. After a review of the existing techniques for dimensionality reduction, several methods are proposed to project the multidimensional data on the plane, minimizing the overlap between classes. These methods allow to project new patterns not used during the projection learning process. Eight types of linear, quadratic and polynomial projections are proposed and combined with four overlapping measures between classes. These projections are compared with another 34 dimensionality reduction methods existing in the literature on a wide collection of 71 benchmark classification problems. The best results have been obtained by the Polynomial Kernel Discriminant Analysis of degree 2 (PKDA2), which creates visual and selfexplanatory maps of the classification problems on which a reference classifier (the support vector machine, or SVM) fails only slightly less than on the original multi-dimensional data. A web interface and a local standalone application are also provided, developed using the PHP and Matlab programming languages, respectively, which allow to apply these projections in order to visualize the 2D maps of any classification problem. In the scope of regression, a wide collection of regressors has been applied for the automatic prediction of temperatures in air conditioning systems (HVAC). These systems have a direct impact on both energy consumption and the comfort of buildings, so an accurate and reliable modelling of the temperature behavior constitutes the starting point for the development of energy efficiency plans. The use of regressors to predict the evolution of indoor temperature of buildings based on internal and external (climatic) conditions allows to evaluate the impact of the modifications in the HVAC systems from a comfort perspective. With the aim of developing an efficient model for HVAC systems, this thesis has evaluated 40 regressors, which belong to 20 different regressor families, using real data generated by an intelligent building, namely the Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS) of the University of Santiago de Compostela (USC). In addition, different models based on neural networks which allow automatic re-training and on-line learning of new data have been developed and compared to the previous 20 off-line regressors. The ability of on-line learning provides robustness to the neural models and allows them to: 1) face circumstances never seen in training due to exceptional climatic situations; and 2) support alterations in the components of the systems produced by errors or changes in the sensor systems.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01
2018
2018-01-01
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10347/16633
url http://hdl.handle.net/10347/16633
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.gl
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.gl
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
repository.name.fl_str_mv
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spelling Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature predictionAlawadi, SadiMaterias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330412 Dispositivos de controlMaterias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330406 Arquitectura de ordenadoresMaterias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120304 Inteligencia artificialThis thesis explores aspects in the field of machine learning, and specifically of pattern classification and regression or function approximation. Although there are many methods of classification for multi-dimensional patterns, in general, they all behave like "black boxes" where the explanation of their operation is difficult or impossible. This thesis develops methods of reducing the dimensionality of data to project multi-dimensional classification problems over a two-dimensional space (a plane). The classifiers can thus be used to learn the projected data and to create two-dimensional maps of classification problems whose graphic nature makes intuitive and easy to understand, helping to explain the classification problem. After a review of the existing techniques for dimensionality reduction, several methods are proposed to project the multidimensional data on the plane, minimizing the overlap between classes. These methods allow to project new patterns not used during the projection learning process. Eight types of linear, quadratic and polynomial projections are proposed and combined with four overlapping measures between classes. These projections are compared with another 34 dimensionality reduction methods existing in the literature on a wide collection of 71 benchmark classification problems. The best results have been obtained by the Polynomial Kernel Discriminant Analysis of degree 2 (PKDA2), which creates visual and selfexplanatory maps of the classification problems on which a reference classifier (the support vector machine, or SVM) fails only slightly less than on the original multi-dimensional data. A web interface and a local standalone application are also provided, developed using the PHP and Matlab programming languages, respectively, which allow to apply these projections in order to visualize the 2D maps of any classification problem. In the scope of regression, a wide collection of regressors has been applied for the automatic prediction of temperatures in air conditioning systems (HVAC). These systems have a direct impact on both energy consumption and the comfort of buildings, so an accurate and reliable modelling of the temperature behavior constitutes the starting point for the development of energy efficiency plans. The use of regressors to predict the evolution of indoor temperature of buildings based on internal and external (climatic) conditions allows to evaluate the impact of the modifications in the HVAC systems from a comfort perspective. With the aim of developing an efficient model for HVAC systems, this thesis has evaluated 40 regressors, which belong to 20 different regressor families, using real data generated by an intelligent building, namely the Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS) of the University of Santiago de Compostela (USC). In addition, different models based on neural networks which allow automatic re-training and on-line learning of new data have been developed and compared to the previous 20 off-line regressors. The ability of on-line learning provides robustness to the neural models and allows them to: 1) face circumstances never seen in training due to exceptional climatic situations; and 2) support alterations in the components of the systems produced by errors or changes in the sensor systems.Fernández Delgado, ManuelMera Pérez, DavidUniversidade de Santiago de Compostela. Departamento de Electrónica e ComputaciónEscola Técnica Superior de EnxeñaríaCentro Singular de Investigación en Tecnoloxías da Información (CiTIUS)20182018-01-0120182018-01-01doctoral thesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisapplication/pdfhttp://hdl.handle.net/10347/16633reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Esta obra atópase baixo unha licenza internacional Creative Commons BY-NC-ND 4.0. Calquera forma de reprodución, distribución, comunicación pública ou transformación desta obra non incluída na licenza Creative Commons BY-NC-ND 4.0 só pode ser realizada coa autorización expresa dos titulares, salvo excepción prevista pola lei. Pode acceder Vde. ao texto completo da licenza nesta ligazón: https://creativecommons.org/licenses/by-nc-nd/4.0/deed.glhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.glinfo:eu-repo/semantics/openAccessoai:minerva.usc.gal:10347/166332026-06-15T12:47:27Z
score 15,812429

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