Removes the majority class member of each pair of observations that form a Tomek link.
Arguments
- df
data.frame or tibble. Must have 1 factor variable and remaining numeric variables.
- var
Character, name of variable containing factor variable.
- distance
A character string specifying the distance metric used for nearest neighbor calculations, defaulting to
"euclidean". The available metrics fall into three groups."euclidean","cosine", and"mahalanobis"use approximate nearest neighbors via the RANN package and scale well to large datasets."squared_chord","matusita","hellinger", and"bhattacharyya"are probability-divergence measures that treat each row as a distribution over the predictors, so they require non-negative values."hellinger"and"bhattacharyya"further require each row to sum to 1. All four also use the RANN package and scale well to large datasets."manhattan","chebyshev","canberra","soergel","lorentzian","jeffreys","topsoe","jensen-shannon","jensen_difference","taneja", and"kumar-johnson"compute an exact all-pairs distance matrix. This takes time and memory proportional to the square of the number of observations in a class, so these are best suited to smaller datasets. Everything from"canberra"onwards is a probability divergence requiring non-negative values, is provided by the philentropy package (which must be installed separately), and in the case of"jeffreys","taneja", and"kumar-johnson"requires strictly positive values, since those divide by individual predictor values.The probability divergences are meaningful for compositional predictors such as proportions or counts normalized per observation, and are generally not appropriate for standardized predictors.
Details
A Tomek link is a pair of points from different classes that are each other's nearest neighbors. Such pairs sit on or very near the decision boundary and are considered noise or borderline cases. The algorithm identifies all Tomek links and removes the majority class instance from each pair, cleaning the class boundary without discarding non-boundary majority examples. Because only boundary points are removed, this typically discards far fewer observations than other under-sampling methods.
All columns used in this function must be numeric with no missing data.
See also
step_tomek() for step function of this method
Other Direct Implementations:
adasyn(),
bsmote(),
cluster_centroids(),
cnn(),
enn(),
instance_hardness(),
kmeans_smote(),
ncl(),
nearmiss(),
oss(),
rose(),
smogn(),
smote(),
smoten(),
smotenc(),
svmsmote()
Examples
circle_numeric <- circle_example[, c("x", "y", "class")]
res <- tomek(circle_numeric, var = "class")
res <- tomek(circle_numeric, var = "class", distance = "manhattan")