% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nearmiss_impl.R
\name{nearmiss}
\alias{nearmiss}
\title{Remove Points Near Other Classes}
\usage{
nearmiss(
  df,
  var,
  k = 5,
  under_ratio = 1,
  distance = "euclidean",
  version = 1,
  n_neighbors_ver3 = 3
)
}
\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{k}{An integer. Number of nearest neighbor that are used
to generate the new examples of the minority class.}

\item{under_ratio}{A numeric value for the ratio of the
majority-to-minority frequencies. The default value (1) means
that all other levels are sampled down to have the same
frequency as the least occurring level. A value of 2 would mean
that the majority levels will have (at most) (approximately)
twice as many rows than the minority level.

A named numeric vector can be used instead to give different levels
different targets, for example \code{c(a = 2, b = 3)}. The names must be levels
of the outcome and the values are ratios of the minority 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
\code{vignette("ratio", package = "themis")} for more details.}

\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.}

\item{version}{An integer. Which of the three NearMiss variants to use,
\code{1}, \code{2}, or \code{3}. Defaults to \code{1}. See the details section.}

\item{n_neighbors_ver3}{An integer. The number of nearest neighbors used
to build the candidate pool of the NearMiss-3 variant. Only used when
\code{version = 3}. Defaults to \code{3}.}
}
\value{
A data.frame or tibble, depending on type of \code{df}.
}
\description{
Generates synthetic positive instances using nearmiss algorithm.
}
\details{
The \code{version} argument selects between the three NearMiss variants:

\describe{
\item{\code{version = 1}}{Retains the points from the majority class which
have the smallest mean distance to their nearest points in the minority
class.}
\item{\code{version = 2}}{Retains the points from the majority class which
have the smallest mean distance to their farthest points in the minority
class.}
\item{\code{version = 3}}{Works in two stages. First, the \code{n_neighbors_ver3}
nearest majority class neighbors of each minority class point form a
candidate pool, and all other majority class points are removed. Then the
points of that pool which have the largest mean distance to their nearest
minority class points are retained.}
}

Since the size of the NearMiss-3 candidate pool is governed by
\code{n_neighbors_ver3} rather than by \code{under_ratio}, the pool can be smaller
than the target set by \code{under_ratio}. The whole pool is then retained and
the target is not reached.

With more than two classes, the mean distance is computed to the nearest
points across all other classes, not only the minority class. This differs
from imbalanced-learn, which measures distance to the minority class only.
The binary case, the primary intended use, is unaffected.

All columns used in this function must be numeric with no missing data.
}
\examples{
circle_numeric <- circle_example[, c("x", "y", "class")]

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

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

res <- nearmiss(circle_numeric, var = "class", under_ratio = 1.5)

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

res <- nearmiss(circle_numeric, var = "class", version = 2)

res <- nearmiss(circle_numeric, var = "class", version = 3)

res <- nearmiss(
  circle_numeric,
  var = "class",
  version = 3,
  n_neighbors_ver3 = 10
)
}
\references{
Inderjeet Mani and I Zhang. knn approach to unbalanced data
distributions: a case study involving information extraction. In Proceedings
of workshop on learning from imbalanced datasets, 2003.
}
\seealso{
\code{\link[=step_nearmiss]{step_nearmiss()}} 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[=cnn]{cnn()}},
\code{\link[=enn]{enn()}},
\code{\link[=instance_hardness]{instance_hardness()}},
\code{\link[=kmeans_smote]{kmeans_smote()}},
\code{\link[=ncl]{ncl()}},
\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}
