adasyn: Adaptive Synthetic Algorithm

In themis: Extra Recipes Steps for Dealing with Unbalanced Data

Adaptive Synthetic Algorithm

Description

Generates synthetic positive instances using ADASYN algorithm.

Usage

adasyn(df, var, k = 5, over_ratio = 1)

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 majority-to-minority 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 than the majority level.

Details

All columns used in this function must be numeric with no missing data.

Value

A data.frame or tibble, depending on type of df.

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.

See Also

step_adasyn() for step function of this method

Other Direct Implementations: bsmote(), nearmiss(), smotenc(), smote(), tomek()

Examples

circle_numeric <- circle_example[, c("x", "y", "class")]

res <- adasyn(circle_numeric, var = "class")

res <- adasyn(circle_numeric, var = "class", k = 10)

res <- adasyn(circle_numeric, var = "class", over_ratio = 0.8)

themis documentation built on Aug. 15, 2023, 1:05 a.m.