Semi-supervised active learning anomaly detection

Detalhes bibliográficos
Autor(a) principal: Santos, Henrique Serafim
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/24745
Resumo: Mestrado Bolonha em Data Analytics for Business
id RCAP_b9558a20ab9bd1e16829ad6cc8dc4888
oai_identifier_str oai:www.repository.utl.pt:10400.5/24745
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Semi-supervised active learning anomaly detectionTime Series Anomaly DetectionFeature Extraction & SelectionSemi Supervised Active LearningQuery Strategy InformativenessMestrado Bolonha em Data Analytics for BusinessThe analysis of Time Series data is a growing field of study due to the increase in the rate of data collection from the most varied sensors that lead to an overload of information to be analysed in order to obtain the most accurate conclusions possible. Hence, due to the high volume of data without labels, automatized detection and labelling of anomalies in Time Series data is an active area of research, as it becomes impossible to manually identify abnormal behavior in Time Series because of the high time and monetary costs. This research focus on the investigation of the power of a Semi Supervised Active Learning algorithm to identify outlier-type anomalies in univariate Time Series. To maximize the performance of the algorithm, we start by proposing an initial pool of features from which the ones with best classification power are selected to develop the algorithm. Regarding the Semi Supervised Learning segment of the process a comparison between several classifiers is made. In addition, various Query Strategies are proposed in the Active Learning segment to increase the informativeness of the observations chosen to be manually labelled so that the time spent labelling anomalies could be decreased without a great impact in the performance of the model. In a first instance, we demonstrate that the pool of designed features better identifies the anomalies than features selected in a fully automatized process. Furthermore, we demonstrate that a Query Strategy used to select the most informative observations to be expertly classified based on the utility and uncertainty of the observations exhibit better results than randomly selecting the observations to be tagged, improving the performance of the model without infeasible time and cost spent in the identification of the anomalous behavior.Instituto Superior de Economia e GestãoBastos, João FerreiraClemente, FabianaRepositório da Universidade de LisboaSantos, Henrique Serafim2022-07-04T09:02:57Z2022-022022-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.5/24745engSantos, Henrique Serafim (2022). “Semi-supervised active learning anomaly detection”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestãoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-06T14:54:22Zoai:www.repository.utl.pt:10400.5/24745Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:08:43.314258Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Semi-supervised active learning anomaly detection
title Semi-supervised active learning anomaly detection
spellingShingle Semi-supervised active learning anomaly detection
Santos, Henrique Serafim
Time Series Anomaly Detection
Feature Extraction & Selection
Semi Supervised Active Learning
Query Strategy Informativeness
title_short Semi-supervised active learning anomaly detection
title_full Semi-supervised active learning anomaly detection
title_fullStr Semi-supervised active learning anomaly detection
title_full_unstemmed Semi-supervised active learning anomaly detection
title_sort Semi-supervised active learning anomaly detection
author Santos, Henrique Serafim
author_facet Santos, Henrique Serafim
author_role author
dc.contributor.none.fl_str_mv Bastos, João Ferreira
Clemente, Fabiana
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Santos, Henrique Serafim
dc.subject.por.fl_str_mv Time Series Anomaly Detection
Feature Extraction & Selection
Semi Supervised Active Learning
Query Strategy Informativeness
topic Time Series Anomaly Detection
Feature Extraction & Selection
Semi Supervised Active Learning
Query Strategy Informativeness
description Mestrado Bolonha em Data Analytics for Business
publishDate 2022
dc.date.none.fl_str_mv 2022-07-04T09:02:57Z
2022-02
2022-02-01T00:00:00Z
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/10400.5/24745
url http://hdl.handle.net/10400.5/24745
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Santos, Henrique Serafim (2022). “Semi-supervised active learning anomaly detection”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestão
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 Instituto Superior de Economia e Gestão
publisher.none.fl_str_mv Instituto Superior de Economia e Gestão
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799131181438992384

Registros relacionados