The R Journal: accepted article

This article will be copy edited and may be changed before publication.

The NoiseFiltersR Package: Label Noise Preprocessing in R
Pablo Morales, Julián Luengo, Luís P.F. Garcia, Ana C. Lorena, André C.P.L.F. de Carvalho and Francisco Herrera

Abstract 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.

Received: 2016-07-12; online 2017-05-10
CRAN packages: MICE, Amelia, caret, FSelector, mvoutlier, robustDA, probFDA, NoiseFiltersR, unbalanced, RWeka , CRAN Task Views implied by cited CRAN packages: MachineLearning, Multivariate, Robust, HighPerformanceComputing, NaturalLanguageProcessing, OfficialStatistics, SocialSciences

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Pablo Morales and Julián Luengo and Luís P.F. Garcia and
          Ana C. Lorena and André C.P.L.F. de Carvalho and Francisco
  title = {{The NoiseFiltersR Package: Label Noise Preprocessing in R}},
  year = {2017},
  journal = {{The R Journal}},
  url = {}