Remove Points Near Other Classes

Generates synthetic positive instances using nearmiss algorithm.

Usage

nearmiss(
  df,
  var,
  k = 5,
  under_ratio = 1,
  distance = "euclidean",
  version = 1,
  n_neighbors_ver3 = 3
)

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 majority-to-minority 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.

A named numeric vector can be used instead to give different levels different targets, for example c(a = 2, b = 3). The names must be levels of the outcome and the values are ratios of the minority 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. See vignette("ratio", package = "themis") for more details.

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.

version

An integer. Which of the three NearMiss variants to use, 1, 2, or 3. Defaults to 1. See the details section.

n_neighbors_ver3

An integer. The number of nearest neighbors used to build the candidate pool of the NearMiss-3 variant. Only used when version = 3. Defaults to 3.

Value

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

Details

The version argument selects between the three NearMiss variants:

version = 1

Retains the points from the majority class which have the smallest mean distance to their nearest points in the minority class.

version = 2

Retains the points from the majority class which have the smallest mean distance to their farthest points in the minority class.

version = 3

Works in two stages. First, the n_neighbors_ver3 nearest majority class neighbors of each minority class point form a candidate pool, and all other majority class points are removed. Then the points of that pool which have the largest mean distance to their nearest minority class points are retained.

Since the size of the NearMiss-3 candidate pool is governed by n_neighbors_ver3 rather than by under_ratio, the pool can be smaller than the target set by under_ratio. The whole pool is then retained and the target is not reached.

With more than two classes, the mean distance is computed to the nearest points across all other classes, not only the minority class. This differs from imbalanced-learn, which measures distance to the minority class only. The binary case, the primary intended use, is unaffected.

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

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(), cluster_centroids(), cnn(), enn(), instance_hardness(), kmeans_smote(), ncl(), oss(), rose(), smogn(), smote(), smoten(), smotenc(), svmsmote(), 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)

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

res <- nearmiss(circle_numeric, var = "class", version = 2)

res <- nearmiss(circle_numeric, var = "class", version = 3)

res <- nearmiss(
  circle_numeric,
  var = "class",
  version = 3,
  n_neighbors_ver3 = 10
)