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Noise detection and elimination for classification problems

Grant number: 11/14602-7
Support type:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): November 01, 2011
Effective date (End): June 30, 2016
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:André Carlos Ponce de Leon Ferreira de Carvalho
Grantee:Luís Paulo Faina Garcia
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated scholarship(s):15/00741-6 - Metaheuristics for label noise identification in classification tasks, BE.EP.DD   13/20983-9 - Complexity measures for noise identification in classification problems, BE.EP.DD


Data collected directly from storage systems often present high rate noise resulting from internal and external factors. When used in the induction of classifiers by machine learning techniques, these noisy data may reduce the predictive accuracy, increase the complexity of the hypothesis obtained and its induction time. This paper aims to investigate two research directions regarding this issue: noise class and predictive attributes noise. Methods like removal, reclassification and imputation of values are used to support the research, as well as machine learning and genetic algorithms based techniques. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MORALES, PABLO; LUENGO, JULIAN; GARCIA, LUIS P. F.; LORENA, ANA C.; DE CARVALHO, ANDRE C. P. L. F.; HERRERA, FRANCISCO. The NoiseFiltersR Package: Label Noise Preprocessing in R. R JOURNAL, v. 9, n. 1, p. 219-228, JUN 2017. Web of Science Citations: 3.
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
GARCIA, Luís Paulo Faina. Noise detection in classification problems. 2016. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação São Carlos.

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