Under-samples the majority classes by keeping only a consistent subset of observations that correctly classifies the data using a 1-nearest-neighbor rule.
cnn(df, var, distance = "euclidean")A data.frame or tibble, depending on type of df.
data.frame or tibble. Must have 1 factor variable and remaining numeric variables.
Character, name of variable containing factor variable.
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.
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.
Hart, P. (1968). The condensed nearest neighbor rule. IEEE Transactions on Information Theory, 14(3), 515-516.
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()
circle_numeric <- circle_example[, c("x", "y", "class")]
res <- cnn(circle_numeric, var = "class")
res <- cnn(circle_numeric, var = "class", distance = "manhattan")
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