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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

The NoiseFiltersR Package: Label Noise Preprocessing in R

Morales, Pablo ; Luengo, Julian ; Garcia, Luis P. F. ; Lorena, Ana C. ; de Carvalho, Andre C. P. L. F. ; Herrera, Francisco
Total Authors: 6
Document type: Journal article
Source: R JOURNAL; v. 9, n. 1, p. 219-228, JUN 2017.
Web of Science Citations: 3

In Data Mining, the value of extracted knowledge is directly related to the quality of the used data. This makes data preprocessing one of the most important steps in the knowledge discovery process. A common problem affecting data quality is the presence of noise. A training set with label noise can reduce the predictive performance of classification learning techniques and increase the overfitting of classification models. In this work we present the NoiseFiltersR package. It contains the first extensive R implementation of classical and state-of-the-art label noise filters, which are the most common techniques for preprocessing label noise. The algorithms used for the implementation of the label noise filters are appropriately documented and referenced. They can be called in a R-user-friendly manner, and their results are unified by means of the ``filter{''} class, which also benefits from adapted print and summary methods. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 12/22608-8 - Use of data complexity measures in the support of supervised machine learning
Grantee:Ana Carolina Lorena
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 11/14602-7 - Noise detection and elimination for classification problems
Grantee:Luís Paulo Faina Garcia
Support type: Scholarships in Brazil - Doctorate (Direct)