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Title: HiSP-GC : a classification method based on probabilistic analysis of patterns.
Authors: Merschmann, Luiz Henrique de Campos
Plastino, Alexandre
Keywords: Classification
Data mining
Issue Date: 2010
Citation: MERSCHMANN, L. H. de C.; PLASTINO, A. HiSP-GC: a classification method based on probabilistic analysis of patterns. Journal of Information and Data Management - JIDM, v. 1, n. 3, p. 423-438, out. 2010. Disponível em: <>. Acesso em: 23 jan. 2015.
Abstract: Classification is one of the most important tasks in data mining and, nowadays, has been applied to solve problems related to different areas, such as administration, finance, education, health and others. Therefore, the construction of precise and computationally efficient classifiers is a relevant challenge in data mining field. In previous works we presented an efficient method for protein classification, called HiSP (Highest Subset Probability) classifier, capable of yielding highly accurate results, outperforming the results obtained by other researchers. Aiming to construct a general purpose classifier based on the ideas explored to solve the protein classification problem, the method previously proposed was adapted and extended. Here we present this expanded and general classification method, called HiSP-GC (HiSP General Classifier), and show that it is appropriate and efficient for several kinds of databases associated with different applications.
ISSN: 2178-7107
metadata.dc.rights.license: Permission to copy without fee all or part of the material printed in JIDM is granted provided that the copies are not made or distributed for commercial advantage, and that notice is given that copying is by permission of the Sociedade Brasileira de Computação. Fonte: Informação contida no artigo.
Appears in Collections:DECOM - Artigos publicados em periódicos

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