2016
DOI: 10.1590/1678-4324-2016160505
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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 syst… Show more

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“…Yazdizadeh et al [44] utilized a Naïve Bayes classifier to classify the category of manufacturing suppliers. Kaijun et al [45] demonstrated document classification using genetic algorithm.…”
Section: Extraction Of Manufacturing Knowledge From Textmentioning
confidence: 99%
“…Yazdizadeh et al [44] utilized a Naïve Bayes classifier to classify the category of manufacturing suppliers. Kaijun et al [45] demonstrated document classification using genetic algorithm.…”
Section: Extraction Of Manufacturing Knowledge From Textmentioning
confidence: 99%