cnn: Condensed Nearest Neighbors

In themis: Extra Recipes Steps for Dealing with Unbalanced Data

Condensed Nearest Neighbors

Description

Under-samples the majority classes by keeping only a consistent subset of observations that correctly classifies the data using a 1-nearest-neighbor rule.

Usage

cnn(df, var, 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.
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.

Details

Condensed Nearest Neighbors (CNN) is an under-sampling method that reduces the majority classes to a consistent subset: a subset that classifies the original data correctly using a 1-nearest-neighbor rule. It starts with a "store" containing all minority class observations and one randomly chosen majority class observation. It then repeatedly scans the remaining majority class observations and moves any that are misclassified by a 1-nearest neighbor fit on the current store into the store. This continues until a full pass adds no new observations. The observations left outside the store are removed.

The smallest class is treated as the minority class and is always kept. CNN tends to keep observations near the decision boundary while discarding redundant interior observations. Because the seed observation and the scan order are random, results depend on the random seed.

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

Hart, P. (1968). The condensed nearest neighbor rule. IEEE Transactions on Information Theory, 14(3), 515-516.

See Also

step_cnn() for step function of this method

Other Direct Implementations: adasyn(), bsmote(), cluster_centroids(), 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 <- cnn(circle_numeric, var = "class")

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

themis documentation built on Aug. 2, 2026, 9:07 a.m.