Description Usage Arguments Details Value References

`SLSMOTE`

over-samples the input data set using the Safe-Level-SMOTE
algorithm.

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 |

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

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

`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. |

Safe-Level-SMOTE works similarly to SMOTE. The difference is that
Safe-Level-SMOTE associates a "level of safeness" to each minority example
and uses this when synthesising new examples. The level of safeness of a
minority example is defined as the number of minority examples among the
`k`

nearest neighbours of the example.

There are several rules used by Safe-Level-SMOTE to synthesise new examples. Apart from the situation where both minority examples have safe levels of zero, and no example is synthesised, the algorithm tends to synthesise new examples closer to the minority examples with safer levels.

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

Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2009).
Safe-level-smote: Safe-level-synthetic minority over-sampling technique
for handling the class imbalanced problem. *Advances in knowledge
discovery and data mining*, 475-482.

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