Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/360 |
Resumo: | Motivated by complex phenomena embedded into time series, this paper proposes EHLAC (Exponential-Weighted Higher-Order Local Auto-Correlation), an approach to extract features from dynamic data based on polynomial relations over time. The main idea for this new approach is to preprocess data in order to improve modeling performance of different techniques. EHLAC extends the traditional HLAC (Higher-Order Local Auto-Correlation), introducing non-linear transformations in terms of its integrals, what inhibits or highlights the influences of observations within the auto-correlation function, highlighting a wider gamut of data characteristics. This approach is evaluated in a song classification scenario, whose results evidence that EHLAC complements the set of attributes of HLAC and improves modeling performance. |
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INFOCOMP: Jornal de Ciência da Computação |
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Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterizationfeature extractionhigh-order local auto-correlationMotivated by complex phenomena embedded into time series, this paper proposes EHLAC (Exponential-Weighted Higher-Order Local Auto-Correlation), an approach to extract features from dynamic data based on polynomial relations over time. The main idea for this new approach is to preprocess data in order to improve modeling performance of different techniques. EHLAC extends the traditional HLAC (Higher-Order Local Auto-Correlation), introducing non-linear transformations in terms of its integrals, what inhibits or highlights the influences of observations within the auto-correlation function, highlighting a wider gamut of data characteristics. This approach is evaluated in a song classification scenario, whose results evidence that EHLAC complements the set of attributes of HLAC and improves modeling performance.Editora da UFLA2012-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/360INFOCOMP Journal of Computer Science; Vol. 11 No. 3-4 (2012): September-December, 2012; 23-301982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/360/344Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessAlbertini, Marcelo KeeseStojmenovic, IvanMello, Rodrigo Fernandes de2015-07-29T14:06:52Zoai:infocomp.dcc.ufla.br:article/360Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:34.256750INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization |
title |
Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization |
spellingShingle |
Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization Albertini, Marcelo Keese feature extraction high-order local auto-correlation |
title_short |
Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization |
title_full |
Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization |
title_fullStr |
Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization |
title_full_unstemmed |
Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization |
title_sort |
Feature Extraction Based on Exponential-Weighted Higher-Order Local Auto-Correlation: An Approach to Improve Data Characterization |
author |
Albertini, Marcelo Keese |
author_facet |
Albertini, Marcelo Keese Stojmenovic, Ivan Mello, Rodrigo Fernandes de |
author_role |
author |
author2 |
Stojmenovic, Ivan Mello, Rodrigo Fernandes de |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Albertini, Marcelo Keese Stojmenovic, Ivan Mello, Rodrigo Fernandes de |
dc.subject.por.fl_str_mv |
feature extraction high-order local auto-correlation |
topic |
feature extraction high-order local auto-correlation |
description |
Motivated by complex phenomena embedded into time series, this paper proposes EHLAC (Exponential-Weighted Higher-Order Local Auto-Correlation), an approach to extract features from dynamic data based on polynomial relations over time. The main idea for this new approach is to preprocess data in order to improve modeling performance of different techniques. EHLAC extends the traditional HLAC (Higher-Order Local Auto-Correlation), introducing non-linear transformations in terms of its integrals, what inhibits or highlights the influences of observations within the auto-correlation function, highlighting a wider gamut of data characteristics. This approach is evaluated in a song classification scenario, whose results evidence that EHLAC complements the set of attributes of HLAC and improves modeling performance. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/360 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/360 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/360/344 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 11 No. 3-4 (2012): September-December, 2012; 23-30 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874741413085184 |