Adaptive Synthetic Algorithm

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 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. See vignette("ratio", package = "themis") for more details.

Value

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

Details

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

References

He, H., Bai, Y., Garcia, E. and Li, S. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference. pp.1322-1328.

See also

step_adasyn() for step function of this method

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