Description Usage Arguments Details Value References

`MWMOTE`

over-samples the input data using the Majority Weighted
Over-Sampling TEchnique.

1 2 3 |

`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 |

`k1` |
Number of neighbours used to identify noisy minority examples. |

`k2` |
Number of neighbours used to identify the borderline majority examples. |

`k3` |
Number of neighbours used to identify the informative minority examples. |

`cut_off` |
Cut-off value to compute the closeness factor. |

`max_closeness` |
Maximum value for the closeness factor. |

`cluster_complexity` |
Value utilised to tune the trade-off between the number of clusters and their size. A large value leads to larger clusters but fewer of them, whereas a small value leads to smaller clusters but more of them. |

`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. |

MWMOTE is a complex over-sampling algorithm and comprises three main phases. First, the hard-to-learn minority examples are identified, then an importance weight is assigned to each of the hard-to-learn examples, and finally new examples are synthesised following a strategy similar to SMOTE.

For clarity, the hyperparameters Cf(th), CMAX, and Cp in the original
description of MWMOTE were renamed here to `cut_off`

,
`max_closeness`

, and `cluster_complexity`

, respectively.

A data frame containing a more balanced version of the input data after over-sampling with the MWMOTE algorithm.

Barua, S., Islam, M. M., Yao, X., & Murase, K. (2014).
MWMOTEâ€“majority weighted minority oversampling technique for imbalanced
data set learning. *IEEE Transactions on Knowledge and Data
Engineering*, *26*(2), 405-425.

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