Description Usage Arguments Details Value References

`NRAS`

removes minority examples that have the proportion of minority
examples among their k nearest neighbours below a threshold.

1 |

`data` |
A data frame containing the predictors and the outcome. The
predictors must be numeric and the outcome must be both a binary valued
factor and the last column of |

`k` |
Number of nearest neighbours to compute for each example in the minority class. |

`classes` |
A named vector identifying the majority and the minority classes. The names must be "Majority" and "Minority". This argument is only useful if the function is called inside another sampling function. |

`theshold` |
All minority examples where the proportion of minority
neighbours is below the |

NRAS fits a logistic regression model to the data and uses it to predict the probability of examples being part of the minority class. These probabilities are included as a new feature of the data and then the minority examples that have few minority examples as their neighbours are removed.

Note that the present implementation does not perform over-sampling with
SMOTE as in the original article. Here, the cleaning was decoupled from the
over-sampling to make NRAS usable with any other over-sampling algorithm.
Therefore, the `sampling_sequence`

function should be used in
conjunction with `NRAS`

to perform over-sampling using any
over-sampling algorithm.

A data frame containing a cleaned version of the input data after using the NRAS algorithm.

Rivera, W. A. (2017). Noise Reduction A Priori Synthetic
Over-Sampling for class imbalanced data sets. *Information Sciences*,
*408*, 146-161.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.