% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cnn_impl.R
\name{cnn}
\alias{cnn}
\title{Condensed Nearest Neighbors}
\usage{
cnn(df, var, distance = "euclidean")
}
\arguments{
\item{df}{data.frame or tibble. Must have 1 factor variable and remaining
numeric variables.}

\item{var}{Character, name of variable containing factor variable.}

\item{distance}{A character string specifying the distance metric used for
nearest neighbor calculations, defaulting to \code{"euclidean"}. The available
metrics fall into three groups.

\code{"euclidean"}, \code{"cosine"}, and \code{"mahalanobis"} use approximate nearest
neighbors via the RANN package and scale well to large datasets.

\code{"squared_chord"}, \code{"matusita"}, \code{"hellinger"}, and \code{"bhattacharyya"} are
probability-divergence measures that treat each row as a distribution over
the predictors, so they require non-negative values. \code{"hellinger"} and
\code{"bhattacharyya"} further require each row to sum to 1. All four also use
the RANN package and scale well to large datasets.

\code{"manhattan"}, \code{"chebyshev"}, \code{"canberra"}, \code{"soergel"}, \code{"lorentzian"},
\code{"jeffreys"}, \code{"topsoe"}, \code{"jensen-shannon"}, \code{"jensen_difference"},
\code{"taneja"}, and \code{"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 \code{"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 \code{"jeffreys"}, \code{"taneja"}, and
\code{"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 \code{df}.
}
\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.
}
\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.
}
\examples{
circle_numeric <- circle_example[, c("x", "y", "class")]

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

res <- cnn(circle_numeric, var = "class", distance = "manhattan")
}
\references{
Hart, P. (1968). The condensed nearest neighbor rule. IEEE
Transactions on Information Theory, 14(3), 515-516.
}
\seealso{
\code{\link[=step_cnn]{step_cnn()}} for step function of this method

Other Direct Implementations:
\code{\link[=adasyn]{adasyn()}},
\code{\link[=bsmote]{bsmote()}},
\code{\link[=cluster_centroids]{cluster_centroids()}},
\code{\link[=enn]{enn()}},
\code{\link[=instance_hardness]{instance_hardness()}},
\code{\link[=kmeans_smote]{kmeans_smote()}},
\code{\link[=ncl]{ncl()}},
\code{\link[=nearmiss]{nearmiss()}},
\code{\link[=oss]{oss()}},
\code{\link[=rose]{rose()}},
\code{\link[=smogn]{smogn()}},
\code{\link[=smote]{smote()}},
\code{\link[=smoten]{smoten()}},
\code{\link[=smotenc]{smotenc()}},
\code{\link[=svmsmote]{svmsmote()}},
\code{\link[=tomek]{tomek()}}
}
\concept{Direct Implementations}
