mlr: smote – R documentation

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smote

Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.

Description

In each iteration, samples one minority class element x1, then one of x1's nearest neighbors: x2. Both points are now interpolated / convex-combined, resulting in a new virtual data point x3 for the minority class.

The method handles factor features, too. The gower distance is used for nearest neighbor calculation, see cluster::daisy. For interpolation, the new factor level for x3 is sampled from the two given levels of x1 and x2 per feature.

Usage

smote(task, rate, nn = 5L, standardize = TRUE, alt.logic = FALSE)

Arguments

`task`	(Task) The task.
`rate`	(`numeric(1)`) Factor to upsample the smaller class. Must be between 1 and `Inf`, where 1 means no oversampling and 2 would mean doubling the class size.
`nn`	(`integer(1)`) Number of nearest neighbors to consider. Default is 5.
`standardize`	(`integer(1)`) Standardize input variables before calculating the nearest neighbors for data sets with numeric input variables only. For mixed variables (numeric and factor) the gower distance is used and variables are standardized anyway. Default is `TRUE`.
`alt.logic`	(`integer(1)`) Use an alternative logic for selection of minority class observations. Instead of sampling a minority class element AND one of its nearest neighbors, each minority class element is taken multiple times (depending on rate) for the interpolation and only the corresponding nearest neighbor is sampled. Default is `FALSE`.

Value

Task.

References

Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, P. (2000) SMOTE: Synthetic Minority Over-sampling TEchnique. In International Conference of Knowledge Based Computer Systems, pp. 46-57. National Center for Software Technology, Mumbai, India, Allied Press.

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release