Improvements on the KNN classifier

Detalhes bibliográficos
Autor(a) principal: Mestre, Ricardo Jorge Palheira
Data de Publicação: 2013
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/10923
Resumo: Dissertação para obtenção do Grau de Mestre em Engenharia Informática
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spelling Improvements on the KNN classifierArtificial intelligenceClassification algorithmsKNNK-nearest neighbor algorithmLazy-learningEager-learningDissertação para obtenção do Grau de Mestre em Engenharia InformáticaThe object classification is an important area within the artificial intelligence and its application extends to various areas, whether or not in the branch of science. Among the other classifiers, the K-nearest neighbor (KNN) is among the most simple and accurate especially in environments where the data distribution is unknown or apparently not parameterizable. This algorithm assigns the classifying element the major class in the K nearest neighbors. According to the original algorithm, this classification implies the calculation of the distances between the classifying instance and each one of the training objects. If on the one hand, having an extensive training set is an element of importance in order to obtain a high accuracy, on the other hand, it makes the classification of each object slower due to its lazy-learning algorithm nature. Indeed, this algorithm does not provide any means of storing information about the previous calculated classifications,making the calculation of the classification of two equal instances mandatory. In a way, it may be said that this classifier does not learn. This dissertation focuses on the lazy-learning fragility and intends to propose a solution that transforms the KNNinto an eager-learning classifier. In other words, it is intended that the algorithm learns effectively with the training set, thus avoiding redundant calculations. In the context of the proposed change in the algorithm, it is important to highlight the attributes that most characterize the objects according to their discriminating power. In this framework, there will be a study regarding the implementation of these transformations on data of different types: continuous and/or categorical.Faculdade de Ciências e TecnologiaSilva, JoaquimRUNMestre, Ricardo Jorge Palheira2014-01-02T11:20:16Z20132013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/10923enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:14:45Zoai:run.unl.pt:10362/10923Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:45:47.737287Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Improvements on the KNN classifier
title Improvements on the KNN classifier
spellingShingle Improvements on the KNN classifier
Mestre, Ricardo Jorge Palheira
Artificial intelligence
Classification algorithms
KNN
K-nearest neighbor algorithm
Lazy-learning
Eager-learning
title_short Improvements on the KNN classifier
title_full Improvements on the KNN classifier
title_fullStr Improvements on the KNN classifier
title_full_unstemmed Improvements on the KNN classifier
title_sort Improvements on the KNN classifier
author Mestre, Ricardo Jorge Palheira
author_facet Mestre, Ricardo Jorge Palheira
author_role author
dc.contributor.none.fl_str_mv Silva, Joaquim
RUN
dc.contributor.author.fl_str_mv Mestre, Ricardo Jorge Palheira
dc.subject.por.fl_str_mv Artificial intelligence
Classification algorithms
KNN
K-nearest neighbor algorithm
Lazy-learning
Eager-learning
topic Artificial intelligence
Classification algorithms
KNN
K-nearest neighbor algorithm
Lazy-learning
Eager-learning
description Dissertação para obtenção do Grau de Mestre em Engenharia Informática
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-01-01T00:00:00Z
2014-01-02T11:20:16Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/10923
url http://hdl.handle.net/10362/10923
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596172416057344

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