Incremental missing data imputation via modified granular evolving fuzzy model
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/30140 |
Resumo: | Large amounts of data have been produced daily. Extracting information and knowledge from data is meaningful for many purposes and endeavors, such as prediction of future values of time series, classification, semi-supervised learning and control. Computational intelligence and machine learning methods, such as neural networks and fuzzy systems, usually require complete datasets to work properly. Real-world datasets may contain missing values due to, e.g., malfunctioning of sensors or data transfer problems. In online environments, the properties of the data may change over time so that offline model training based on multiple passes over data is prohibited due to its inherent time and memory constraints. This study proposes a method for incremental missing data imputation using a modified granular evolving fuzzy model, namely evolving Fuzzy Granular Predictor (eFGP). eFGP is equipped with an incremental learning algorithm that simultaneously impute missing data and adapt model parameters and structure. eFGP is able to handle single and multiple missing values on data samples by developing reduced-term consequent polynomials and relying on information of time-varying granules. The method is evaluated in prediction and function approximation problems considering the constraints of online data stream. Particularly, the underlying data streams may be subject to missing at random (MAR) and missing completely at random (MCAR) types of missing values. Predictions given by the model evolved after data imputation are compared to those provided by state-of-the-art fuzzy and neuro-fuzzy evolving modeling methods in the sense of accuracy. Results and statistical comparisons with other approaches corroborate to conclude that eFGP is competitive as a general evolving intelligent method and overcomes its counterparts in MAR and MCAR scenarios according to an ANOVA-Tukey statistical hypothesis test. |
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Incremental missing data imputation via modified granular evolving fuzzy modelImputação incremental de dados faltantes via modelo granular fuzzy evolutivo modificadoEvolving intelligenceFuzzy systemsData streamIncremental learningMissing data imputationInteligência em evoluçãoSistemas FuzzyFluxo de dadosAprendizagem incrementalImputação de dados perdidosEngenharia de SoftwareLarge amounts of data have been produced daily. Extracting information and knowledge from data is meaningful for many purposes and endeavors, such as prediction of future values of time series, classification, semi-supervised learning and control. Computational intelligence and machine learning methods, such as neural networks and fuzzy systems, usually require complete datasets to work properly. Real-world datasets may contain missing values due to, e.g., malfunctioning of sensors or data transfer problems. In online environments, the properties of the data may change over time so that offline model training based on multiple passes over data is prohibited due to its inherent time and memory constraints. This study proposes a method for incremental missing data imputation using a modified granular evolving fuzzy model, namely evolving Fuzzy Granular Predictor (eFGP). eFGP is equipped with an incremental learning algorithm that simultaneously impute missing data and adapt model parameters and structure. eFGP is able to handle single and multiple missing values on data samples by developing reduced-term consequent polynomials and relying on information of time-varying granules. The method is evaluated in prediction and function approximation problems considering the constraints of online data stream. Particularly, the underlying data streams may be subject to missing at random (MAR) and missing completely at random (MCAR) types of missing values. Predictions given by the model evolved after data imputation are compared to those provided by state-of-the-art fuzzy and neuro-fuzzy evolving modeling methods in the sense of accuracy. Results and statistical comparisons with other approaches corroborate to conclude that eFGP is competitive as a general evolving intelligent method and overcomes its counterparts in MAR and MCAR scenarios according to an ANOVA-Tukey statistical hypothesis test.Não se aplica.Universidade Federal de LavrasPrograma de Pós-Graduação em Engenharia de Sistemas e AutomaçãoUFLAbrasilDepartamento de EngenhariaLeite, Daniel FurtadoEsmin, Ahmed Ali AbdallaCamargo, Heloisa de ArrudaCintra, Marcos EvandroGarcia, Cristiano Mesquita2018-08-23T11:45:29Z2018-08-23T11:45:29Z2018-08-222018-07-17info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfGARCIA, C. M. Incremental missing data imputation via modified granular evolving fuzzy model. 2018. 71 p. Dissertação (Mestrado em Engenharia de Sistemas e Automação)-Universidade Federal de Lavras, Lavras, 2018.http://repositorio.ufla.br/jspui/handle/1/30140enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLA2018-08-23T11:45:30Zoai:localhost:1/30140Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2018-08-23T11:45:30Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Incremental missing data imputation via modified granular evolving fuzzy model Imputação incremental de dados faltantes via modelo granular fuzzy evolutivo modificado |
title |
Incremental missing data imputation via modified granular evolving fuzzy model |
spellingShingle |
Incremental missing data imputation via modified granular evolving fuzzy model Garcia, Cristiano Mesquita Evolving intelligence Fuzzy systems Data stream Incremental learning Missing data imputation Inteligência em evolução Sistemas Fuzzy Fluxo de dados Aprendizagem incremental Imputação de dados perdidos Engenharia de Software |
title_short |
Incremental missing data imputation via modified granular evolving fuzzy model |
title_full |
Incremental missing data imputation via modified granular evolving fuzzy model |
title_fullStr |
Incremental missing data imputation via modified granular evolving fuzzy model |
title_full_unstemmed |
Incremental missing data imputation via modified granular evolving fuzzy model |
title_sort |
Incremental missing data imputation via modified granular evolving fuzzy model |
author |
Garcia, Cristiano Mesquita |
author_facet |
Garcia, Cristiano Mesquita |
author_role |
author |
dc.contributor.none.fl_str_mv |
Leite, Daniel Furtado Esmin, Ahmed Ali Abdalla Camargo, Heloisa de Arruda Cintra, Marcos Evandro |
dc.contributor.author.fl_str_mv |
Garcia, Cristiano Mesquita |
dc.subject.por.fl_str_mv |
Evolving intelligence Fuzzy systems Data stream Incremental learning Missing data imputation Inteligência em evolução Sistemas Fuzzy Fluxo de dados Aprendizagem incremental Imputação de dados perdidos Engenharia de Software |
topic |
Evolving intelligence Fuzzy systems Data stream Incremental learning Missing data imputation Inteligência em evolução Sistemas Fuzzy Fluxo de dados Aprendizagem incremental Imputação de dados perdidos Engenharia de Software |
description |
Large amounts of data have been produced daily. Extracting information and knowledge from data is meaningful for many purposes and endeavors, such as prediction of future values of time series, classification, semi-supervised learning and control. Computational intelligence and machine learning methods, such as neural networks and fuzzy systems, usually require complete datasets to work properly. Real-world datasets may contain missing values due to, e.g., malfunctioning of sensors or data transfer problems. In online environments, the properties of the data may change over time so that offline model training based on multiple passes over data is prohibited due to its inherent time and memory constraints. This study proposes a method for incremental missing data imputation using a modified granular evolving fuzzy model, namely evolving Fuzzy Granular Predictor (eFGP). eFGP is equipped with an incremental learning algorithm that simultaneously impute missing data and adapt model parameters and structure. eFGP is able to handle single and multiple missing values on data samples by developing reduced-term consequent polynomials and relying on information of time-varying granules. The method is evaluated in prediction and function approximation problems considering the constraints of online data stream. Particularly, the underlying data streams may be subject to missing at random (MAR) and missing completely at random (MCAR) types of missing values. Predictions given by the model evolved after data imputation are compared to those provided by state-of-the-art fuzzy and neuro-fuzzy evolving modeling methods in the sense of accuracy. Results and statistical comparisons with other approaches corroborate to conclude that eFGP is competitive as a general evolving intelligent method and overcomes its counterparts in MAR and MCAR scenarios according to an ANOVA-Tukey statistical hypothesis test. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-08-23T11:45:29Z 2018-08-23T11:45:29Z 2018-08-22 2018-07-17 |
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 |
GARCIA, C. M. Incremental missing data imputation via modified granular evolving fuzzy model. 2018. 71 p. Dissertação (Mestrado em Engenharia de Sistemas e Automação)-Universidade Federal de Lavras, Lavras, 2018. http://repositorio.ufla.br/jspui/handle/1/30140 |
identifier_str_mv |
GARCIA, C. M. Incremental missing data imputation via modified granular evolving fuzzy model. 2018. 71 p. Dissertação (Mestrado em Engenharia de Sistemas e Automação)-Universidade Federal de Lavras, Lavras, 2018. |
url |
http://repositorio.ufla.br/jspui/handle/1/30140 |
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 |
Universidade Federal de Lavras Programa de Pós-Graduação em Engenharia de Sistemas e Automação UFLA brasil Departamento de Engenharia |
publisher.none.fl_str_mv |
Universidade Federal de Lavras Programa de Pós-Graduação em Engenharia de Sistemas e Automação UFLA brasil Departamento de Engenharia |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835053738164224 |