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Edited Nearest Neighbors

Source: R/enn.R
step_enn.Rd

step_enn() creates a specification of a recipe step that removes observations whose class differs from the majority of their nearest neighbors.

Usage

step_enn(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  neighbors = 3,
  distance = "euclidean",
  times = 1,
  all_k = FALSE,
  kind_sel = "mode",
  skip = TRUE,
  seed = sample.int(10^5, 1),
  distance_with = recipes::all_predictors(),
  id = rand_id("enn")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variable is used to sample the data. See recipes::selections for more details. The selection should result in single factor variable. For the tidy method, these are not currently used.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

column

A character string of the variable name that will be populated (eventually) by the ... selectors.

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.

skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.

seed

An integer that will be used as the seed when applied.

distance_with

A call to a selector function to choose which variables are used for distance calculations. Defaults to recipes::all_predictors(). The variable selected by ... is always excluded from the distance calculations.

id

A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms which is the variable used to sample.

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 variables selected by distance_with must be numeric with no missing data.

All columns in the data are sampled and returned by recipes::juice() and recipes::bake().

When used in modeling, users should strongly consider using the option skip = TRUE so that the extra sampling is not conducted outside of the training set.

Minimum observations

The data must have at least neighbors + 1 observations for the nearest neighbors to be computed.

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms

character, the selectors or variables selected

id

character, id of this step

Tuning Parameters

This step has 2 tuning parameters:

  • neighbors: # Nearest Neighbors (type: integer, default: 3)

  • all_k: Pruning (type: logical, default: FALSE)

Case weights

The underlying operation does not allow for case weights. Supplying data with a case weights column to this step results in an error.

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

enn() for direct implementation

step_smote(), which is commonly composed before step_enn() to clean the ambiguous points that over-sampling creates near the class boundary (the equivalent of imbalanced-learn's SMOTEENN).

Other Steps for under-sampling: step_cluster_centroids(), step_cnn(), step_downsample(), step_instance_hardness(), step_ncl(), step_nearmiss(), step_oss(), step_tomek()

Examples

library(recipes)
library(modeldata)
data(hpc_data)

hpc_data0 <- hpc_data |>
  select(-protocol, -day)

orig <- count(hpc_data0, class, name = "orig")
orig
#> # A tibble: 4 × 2
#>   class  orig
#>   <fct> <int>
#> 1 VF     2211
#> 2 F      1347
#> 3 M       514
#> 4 L       259

up_rec <- recipe(class ~ ., data = hpc_data0) |>
  step_enn(class) |>
  prep()

training <- up_rec |>
  bake(new_data = NULL) |>
  count(class, name = "training")
training
#> # A tibble: 4 × 2
#>   class training
#>   <fct>    <int>
#> 1 VF        1737
#> 2 F          731
#> 3 M          262
#> 4 L          173

# Since `skip` defaults to TRUE, baking the step has no effect
baked <- up_rec |>
  bake(new_data = hpc_data0) |>
  count(class, name = "baked")
baked
#> # A tibble: 4 × 2
#>   class baked
#>   <fct> <int>
#> 1 VF     2211
#> 2 F      1347
#> 3 M       514
#> 4 L       259

orig |>
  left_join(training, by = "class") |>
  left_join(baked, by = "class")
#> # A tibble: 4 × 4
#>   class  orig training baked
#>   <fct> <int>    <int> <int>
#> 1 VF     2211     1737  2211
#> 2 F      1347      731  1347
#> 3 M       514      262   514
#> 4 L       259      173   259

library(ggplot2)

ggplot(circle_example, aes(x, y, color = class)) +
  geom_point() +
  labs(title = "Without ENN") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))


recipe(class ~ x + y, data = circle_example) |>
  step_enn(class) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With ENN") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))