Description Usage Arguments Details Value References

`BDLSMOTE`

over-samples the input data using either borderline-SMOTE1
or borderline-SMOTE2 algorithms.

1 2 |

`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 |

`over_replace` |
A logical value indicating whether the neighbours
picked from the |

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

`m` |
Number of neighbours used to decide whether a minority example is borderline or not. The authors call the set of all borderline examples the DANGER set. |

`borderline` |
Select between borderline-SMOTE1 and borderline-SMOTE2. Possible values are 1 and 2. |

`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. |

Borderline-SMOTE{1, 2} algorithms work similarly to SMOTE, however, they only synthesise new examples using the minority examples that are borderline.

A data frame containing a more balanced version of the input data after over-sampling with either borderline-SMOTE1 or borderline-SMOTE2.

Han, H., Wang, W. Y., & Mao, B. H. (2005, August).
Borderline-SMOTE: a new over-sampling method in imbalanced data sets
learning. In *International Conference on Intelligent Computing*
(pp. 878-887). Springer Berlin Heidelberg.

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