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Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/12289

Title: Analysis of machine learning techniques to classify news for information management in coffee market
Other Titles: Análise de técnicas de aprendizado de máquina para classificar notícias para gerencimento de informação no mercado de café
???metadata.dc.creator???: Lima Júnior, Paulo Oliveira
Castro Júnior, Luiz Gonzada de
Zambalde, André Luiz
Keywords: Gerenciamento de recursos de informação
Café
Sistema computacional
Árvore de decisão
Classificador Naive Bayes
Máquinas de vetores de suporte
Information resources management
Coffee
Computational system
Decision tree
Naïve Bayes classifier
Support vector machines
Publisher: IEEE América Latina
Issue Date: Jul-2015
Citation: LIMA JÚNIOR, P. O.; CASTRO JÚNIOR, L. G. de; ZAMBALDE, A. L. Analysis of machine learning techniques to classify news for information management in coffee market. Revista do IEEE América Latina, [S. l.], v. 13, n. 7, p. 2285-2291, July 2015. Texto em português.
Abstract: This paper presents an empirical study of machine learn techniques to text categorization. Specifically aim to classify news about coffee market according with categories from coffee supply chain. The objective is to measure the performance of three types of algorithms: Naïve Bayes based, Tree bases and Support Vector Machine (SVM). A database with news collected from web and labeled by human expert analysts is used in a learning phase. Then automatic classify news extracted from web following the same steps and terms as human according to their relevance for each learned category. The test in a real database shows a better performance by Naïve Bayes based Algorithms for this specific case.
URI: http://www.ewh.ieee.org/reg/9/etrans/ieee/issues/vol13/vol13issue07July2015/36OliveiraLimaJunior.htm
http://repositorio.ufla.br/jspui/handle/1/12289
???metadata.dc.language???: pt_BR
Appears in Collections:DCC - Artigos publicados em periódicos

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