Research on manufacturing text classification based on improved genetic algorithm
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Archives of Biology and Technology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000200600 |
Resumo: | ABSTRACT According to the features of texts, a text classification model is proposed. Base on this model, an optimized objective function is designed by utilizing the occurrence frequency of each feature in each category. According to the relation matrix oftext resource and features, an improved genetic algorithm is adopted for solution with integral matrix crossover, transposition and recombination of entire population. At last the sample date of manufacturing text information from professional resources database system is taken as an example to illustrate the proposed model and solution for feature dimension reduction and text classification. The crossover and mutation probabilities of algorithm are compared vertically and horizontally to determine a group of better parameters. The experiment results show that the proposed method is fast and effective. |
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Brazilian Archives of Biology and Technology |
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Research on manufacturing text classification based on improved genetic algorithmText classificationgenetic algorithmdimension reductiontext classificationmanufacturing textABSTRACT According to the features of texts, a text classification model is proposed. Base on this model, an optimized objective function is designed by utilizing the occurrence frequency of each feature in each category. According to the relation matrix oftext resource and features, an improved genetic algorithm is adopted for solution with integral matrix crossover, transposition and recombination of entire population. At last the sample date of manufacturing text information from professional resources database system is taken as an example to illustrate the proposed model and solution for feature dimension reduction and text classification. The crossover and mutation probabilities of algorithm are compared vertically and horizontally to determine a group of better parameters. The experiment results show that the proposed method is fast and effective.Instituto de Tecnologia do Paraná - Tecpar2016-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000200600Brazilian Archives of Biology and Technology v.59 n.spe 2016reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2016160505info:eu-repo/semantics/openAccessKaijun,ZhouYifei,Tongeng2016-10-18T00:00:00Zoai:scielo:S1516-89132016000200600Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2016-10-18T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.fl_str_mv |
Research on manufacturing text classification based on improved genetic algorithm |
title |
Research on manufacturing text classification based on improved genetic algorithm |
spellingShingle |
Research on manufacturing text classification based on improved genetic algorithm Kaijun,Zhou Text classification genetic algorithm dimension reduction text classification manufacturing text |
title_short |
Research on manufacturing text classification based on improved genetic algorithm |
title_full |
Research on manufacturing text classification based on improved genetic algorithm |
title_fullStr |
Research on manufacturing text classification based on improved genetic algorithm |
title_full_unstemmed |
Research on manufacturing text classification based on improved genetic algorithm |
title_sort |
Research on manufacturing text classification based on improved genetic algorithm |
author |
Kaijun,Zhou |
author_facet |
Kaijun,Zhou Yifei,Tong |
author_role |
author |
author2 |
Yifei,Tong |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Kaijun,Zhou Yifei,Tong |
dc.subject.por.fl_str_mv |
Text classification genetic algorithm dimension reduction text classification manufacturing text |
topic |
Text classification genetic algorithm dimension reduction text classification manufacturing text |
description |
ABSTRACT According to the features of texts, a text classification model is proposed. Base on this model, an optimized objective function is designed by utilizing the occurrence frequency of each feature in each category. According to the relation matrix oftext resource and features, an improved genetic algorithm is adopted for solution with integral matrix crossover, transposition and recombination of entire population. At last the sample date of manufacturing text information from professional resources database system is taken as an example to illustrate the proposed model and solution for feature dimension reduction and text classification. The crossover and mutation probabilities of algorithm are compared vertically and horizontally to determine a group of better parameters. The experiment results show that the proposed method is fast and effective. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000200600 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000200600 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-2016160505 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
dc.source.none.fl_str_mv |
Brazilian Archives of Biology and Technology v.59 n.spe 2016 reponame:Brazilian Archives of Biology and Technology instname:Instituto de Tecnologia do Paraná (Tecpar) instacron:TECPAR |
instname_str |
Instituto de Tecnologia do Paraná (Tecpar) |
instacron_str |
TECPAR |
institution |
TECPAR |
reponame_str |
Brazilian Archives of Biology and Technology |
collection |
Brazilian Archives of Biology and Technology |
repository.name.fl_str_mv |
Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar) |
repository.mail.fl_str_mv |
babt@tecpar.br||babt@tecpar.br |
_version_ |
1750318277761957888 |