Borderline-SMOTE Algorithm

BSMOTE generates new examples of the minority class using nearest neighbors of these cases in the border region between classes.

Usage

bsmote(
  df,
  var,
  k = 5,
  over_ratio = 1,
  all_neighbors = FALSE,
  distance = "euclidean"
)

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

all_neighbors

Type of two borderline-SMOTE method. Defaults to FALSE. See 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.

Value

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

Details

BSMOTE (borderline-SMOTE) works the same way as SMOTE, except that instead of generating points around every point of the minority class each point is first classified into the boxes "danger" and "not". For each point the nearest neighbors are calculated. If all the neighbors come from a different class it is labeled noise and put into the "not" box. If more than half of the neighbors come from a different class it is labeled "danger". Points are generated around points labeled "danger".

If all_neighbors = FALSE then points are generated between nearest neighbors in its own class. If all_neighbors = TRUE then points are generated between any nearest neighbors. See examples for visualization.

SMOTE generates new examples of the minority class using nearest neighbors of these cases. For each existing minority class example, new examples are created by interpolating between the example and its nearest neighbors. The number of nearest neighbors used is controlled by the number of neighbors argument (k in smote(), neighbors in step_smote()), and the number of new examples generated is controlled by over_ratio.

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

References

Hui Han, Wen-Yuan Wang, and Bing-Huan Mao. Borderline-smote: a new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing, pages 878–887. Springer, 2005.

See also

step_bsmote() for step function of this method

Other Direct Implementations: adasyn(), cluster_centroids(), cnn(), enn(), instance_hardness(), kmeans_smote(), ncl(), nearmiss(), oss(), rose(), smogn(), smote(), smoten(), smotenc(), svmsmote(), tomek()

Examples

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

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

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

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

res <- bsmote(circle_numeric, var = "class", all_neighbors = TRUE)

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