Description Usage Arguments Details Value References

`OSS`

under-samples the input data using the One-Sided Selection
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 |

`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. |

OSS first reduces the original data set into a consistent subset and then removes all majority examples that belong to Tomek Links. To find a consistent subset, OSS creates a subset of the data containing a random example from majority class and all examples from minority class, and adds to this subset all majority examples that are missclassified by this subset using the 1-NN rule.

A data frame containing a more balanced version of the input data after under-sampling it with OSS.

Kubat, M., & Matwin, S. (1997, July). Addressing the curse of
imbalanced training sets: one-sided selection. In *ICML* (Vol. 97,
pp. 179-186).

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