Statistics, Department of

 

The R Journal

Date of this Version

6-2017

Document Type

Article

Citation

The R Journal (June 2017) 9(1); Editor: Roger Bivand

Comments

Copyright 2017, The R Foundation. Open access material. License: CC BY 4.0 International

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.

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