RUS: The Random Under-Sampling algorithm.
In RomeroBarata/bimba: Sampling Algorithms for Two-Class Imbalanced Data Sets

Description Usage Arguments Details Value Examples

RUS returns a more balanced version of a data set after application of the Random Under-Sampling algorithm.

1	RUS(data, perc_maj = 50, perc_under = NULL, classes = NULL)

`data`	A data frame containing the predictors and the outcome. The outcome must be both a binary valued factor and the last column of `data`.
`perc_maj`	The desired % size of the majority class relative to the whole data set. For instance, if `perc_maj` = 50 a balanced version of the input data set is returned. `perc_maj` is ignored if `perc_under` is specified.
`perc_under`	% of examples to select from the majority class. If specified `perc_maj` is ignored.
`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.

The Random Under-Sampling algorithm creates a new data set containing all examples from the minority class plus a random selection of examples from the majority class.

A data frame containing a more balanced version of the input data set after application of the Random Under-Sampling algorithm. The original order of the examples is preserved.

imb_data <- generate_imbalanced_data(num_examples = 200, 
                                     num_features = 2,
                                     imbalance_ratio = 5,
                                     noise_maj = 0,
                                     noise_min = 0,
                                     seed = 42)
 
table(imb_data$target)
table(RUS(imb_data, perc_maj = 50)$target)    # Balance the classes
table(RUS(imb_data, perc_under = 20)$target)  # Select 20% of maj. class