Research on manufacturing text classification based on improved genetic algorithm
Braz. arch. biol. technol
;
59(spe): e16160505, 2016. tab, graf
Artigo
em Inglês
| LILACS
| ID: lil-796859
ABSTRACT
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.
Texto completo:
DisponíveL
Índice:
LILACS (Américas)
Tipo de estudo:
Estudo prognóstico
Idioma:
Inglês
Revista:
Braz. arch. biol. technol
Assunto da revista:
Biologia
Ano de publicação:
2016
Tipo de documento:
Artigo
País de afiliação:
China
Instituição/País de afiliação:
Nantong Vocational University/CN
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