Description Usage Arguments Details Value References

`RWO`

over-samples the input data using the Random Walk Over-Sampling
algorithm.

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 |

`perc_min` |
The desired % size of the minority class relative to the
whole data set. For instance, if |

`perc_over` |
% of examples to append to the input data set relative
to the size of the minority class. For instance, if |

`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. |

Random Walk Over-Sampling is based on the Central Limit Theorem and synthesises new examples for the minority class by perturbing the available minority examples.

A data frame containing a more balanced version of the input data after over-sampling with the Random Walk Over-Sampling algorithm.

Zhang, H., & Li, M. (2014). RWO-Sampling: A random walk
over-sampling approach to imbalanced data classification.
*Information Fusion*, *20*, 99-116.

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