SMOTENC generates new examples of the minority class using nearest neighbors of these cases, and can handle categorical variables
Arguments
- df
data.frame or tibble. Must have 1 factor variable and remaining numeric variables.
- var
Character, name of variable containing factor variable.
- k
An integer. Number of nearest neighbor that are used to generate the new examples of the minority class.
- over_ratio
A numeric value for the ratio of the minority-to-majority frequencies. The default value (1) means that all other levels are sampled up to have the same frequency as the most occurring level. A value of 0.5 would mean that the minority levels will have (at most) (approximately) half as many rows as the majority level.
A named numeric vector can be used instead to give different levels different targets, for example
c(a = 1, b = 0.5). The names must be levels of the outcome and the values are ratios of the majority level, exactly as in the single-number case. Levels that are not named are left untouched, as are rows with a missing outcome. Because a vector of targets is not a single value, supplying one means this argument can no longer be tuned. Seevignette("ratio", package = "themis")for more details.
Details
SMOTENC extends SMOTE to handle data sets with a mix of numeric and
categorical predictors. For each minority class example, new synthetic
examples are generated by interpolating between the example and its nearest
neighbors using Gower's distance. Numeric features are interpolated
continuously; categorical features take the most common value among the
neighbors. The number of new examples generated is controlled by
over_ratio.
Columns can be numeric and categorical with no missing data.
References
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357.
Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics 27(4):857-871. (For the distance metric used)
See also
step_smotenc() 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(),
svmsmote(),
tomek()
Examples
circle_numeric <- circle_example[, c("x", "y", "class")]
res <- smotenc(circle_numeric, var = "class")
res <- smotenc(circle_numeric, var = "class", k = 10)
res <- smotenc(circle_numeric, var = "class", over_ratio = 0.8)