Under-samples the majority classes by removing the points that are hardest to classify.
Arguments
- df
data.frame or tibble. Must have 1 factor variable and remaining numeric variables.
- var
Character, name of variable containing factor variable.
- k
An integer. Number of nearest neighbors used to estimate the instance hardness of each observation.
- 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
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. Seevignette("ratio", package = "themis")for more details.- 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.
Details
The instance hardness of each observation is estimated using the
k-Disagreeing Neighbors measure: the proportion of the nearest neighbors
that belong to a different class. Observations that are surrounded by points
of a different class are considered hard to classify. For each majority
class, the hardest observations are removed until the desired under_ratio
is reached.
All columns used in this function must be numeric with no missing data.
References
Smith, M. R., Martinez, T., & Giraud-Carrier, C. (2014). An instance level analysis of data complexity. Machine learning, 95(2), 225-256.
See also
step_instance_hardness() for step function of this method
Other Direct Implementations:
adasyn(),
bsmote(),
cluster_centroids(),
cnn(),
enn(),
kmeans_smote(),
ncl(),
nearmiss(),
oss(),
rose(),
smogn(),
smote(),
smoten(),
smotenc(),
svmsmote(),
tomek()
Examples
circle_numeric <- circle_example[, c("x", "y", "class")]
res <- instance_hardness(circle_numeric, var = "class")
res <- instance_hardness(circle_numeric, var = "class", k = 10)
res <- instance_hardness(circle_numeric, var = "class", under_ratio = 1.5)
res <- instance_hardness(circle_numeric, var = "class", distance = "manhattan")