nearmiss: Remove Points Near Other Classes

In themis: Extra Recipes Steps for Dealing with Unbalanced Data

Remove Points Near Other Classes

Description

Generates synthetic positive instances using nearmiss algorithm.

Usage

nearmiss(df, var, k = 5, under_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.
under_ratio	A numeric value for the ratio of the minority-to-majority frequencies. The default value (1) means that all other levels are sampled down to have the same frequency as the least occurring level. A value of 2 would mean that the majority levels will have (at most) (approximately) twice as many rows than the minority 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

Inderjeet Mani and I Zhang. knn approach to unbalanced data distributions: a case study involving information extraction. In Proceedings of workshop on learning from imbalanced datasets, 2003.

See Also

step_nearmiss() for step function of this method

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

Examples

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

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

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

res <- nearmiss(circle_numeric, var = "class", under_ratio = 1.5)

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