RWO: The Random Walk Over-Sampling algorithm.

Description Usage Arguments Details Value References


RWO over-samples the input data using the Random Walk Over-Sampling algorithm.


RWO(data, perc_min = 50, perc_over = NULL, classes = NULL)



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 data.


The desired % size of the minority class relative to the whole data set. For instance, if perc_min = 50 the returned data set is balanced. perc_min is ignored if perc_over is specified.


% of examples to append to the input data set relative to the size of the minority class. For instance, if perc_over = 100 the minority class doubles in size. If specified, perc_min is ignored.


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.


Random Walk Over-Sampling is based on the Central Limit Theorem and synthesises new examples for the minority class by perturbing the available minority examples.


A data frame containing a more balanced version of the input data after over-sampling with the Random Walk Over-Sampling algorithm.


Zhang, H., & Li, M. (2014). RWO-Sampling: A random walk over-sampling approach to imbalanced data classification. Information Fusion, 20, 99-116.

RomeroBarata/bimba documentation built on May 17, 2019, 8:03 a.m.