themis (version 1.1.0)

enn: Edited Nearest Neighbors

Description

Removes observations whose class differs from the majority of their nearest neighbors.

Usage

enn(
  df,
  var,
  neighbors = 3,
  distance = "euclidean",
  times = 1,
  all_k = FALSE,
  kind_sel = "mode"
)

Value

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

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.

times

A positive integer for the maximum number of times ENN is applied. Defaults to 1 for a single pass. Values greater than 1 repeat the cleaning, stopping early once a pass removes no observations. Use Inf to repeat until convergence (Repeated Edited Nearest Neighbors).

all_k

A logical. When TRUE, ENN is applied with an increasing number of neighbors, from 1 up to neighbors, cleaning the data at each step (All k-Nearest Neighbors). Takes precedence over times. Defaults to FALSE.

kind_sel

A character string. The rule used to decide whether an observation is removed. "mode" (the default) removes an observation when the majority of its neighbors disagree with its class. "all" is stricter and removes an observation unless all of its neighbors share its class.

Details

Edited Nearest Neighbors (ENN) is a cleaning method. For each observation it finds the neighbors nearest neighbors and, if the class of the observation does not match the majority class among those neighbors, the observation is removed. This tends to remove noisy and borderline observations, which can lead to smoother decision boundaries.

Setting times greater than 1 applies ENN repeatedly, removing more noisy and borderline observations on each pass and stopping early once a pass removes nothing. This corresponds to Repeated Edited Nearest Neighbors (RENN).

Setting all_k = TRUE applies ENN with increasing numbers of neighbors, from 1 up to neighbors, cleaning the data at each step. This corresponds to All k-Nearest Neighbors (AllKNN) and takes precedence over times.

Setting kind_sel = "all" uses a stricter cleaning rule: instead of removing an observation when the majority of its neighbors disagree, it is removed unless every one of its neighbors shares its class. This removes more observations than the default kind_sel = "mode".

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

Use times = Inf to repeat ENN until convergence.

References

Wilson, D. L. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics, (3), 408-421.

Tomek, I. (1976). An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, (6), 448-452.

See Also

step_enn() for step function of this method

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

Examples

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

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

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

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

# Repeated Edited Nearest Neighbors (RENN)
res <- enn(circle_numeric, var = "class", times = Inf)

# All k-Nearest Neighbors (AllKNN)
res <- enn(circle_numeric, var = "class", all_k = TRUE)

# Stricter cleaning: remove unless all neighbors agree
res <- enn(circle_numeric, var = "class", kind_sel = "all")