Neighborhood Cleaning Rule

Under-samples the majority classes by cleaning noisy observations and observations that pollute the neighborhood of minority class observations.

Usage

ncl(df, var, neighbors = 3, distance = "euclidean", threshold_clean = 0.5)

Arguments

df

data.frame or tibble. Must have 1 factor variable and remaining numeric variables.

var

Character, name of variable containing factor variable.

neighbors

An integer. Number of nearest neighbor that are used to decide whether an observation is removed. Defaults to 3, unlike the over-sampling steps which default to 5.

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.

threshold_clean

A numeric. Majority classes are only cleaned around minority class observations when their size is greater than threshold_clean times the size of the minority class. Defaults to 0.5.

Value

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

Details

The Neighborhood Cleaning Rule (NCL) is a cleaning method that combines two passes over the data. First, it applies the Edited Nearest Neighbors rule, removing majority class observations whose class differs from the majority of their neighbors nearest neighbors. Second, for each minority class observation that is itself misclassified by its neighbors, the majority class observations among those neighbors are removed. Compared to Edited Nearest Neighbors, this focuses the cleaning on the neighborhoods of minority class observations.

The smallest class is treated as the minority class. Only majority classes larger than threshold_clean times the size of the minority class are cleaned in the second pass.

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

References

Laurikkala, J. (2001). Improving identification of difficult small classes by balancing class distribution. In Conference on Artificial Intelligence in Medicine in Europe (pp. 63-66). Springer.

See also

step_ncl() for step function of this method

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

Examples

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

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

res <- ncl(circle_numeric, var = "class", neighbors = 5)

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