neater: Fitering of oversampled data based on non-cooperative game...
In imbalance: Preprocessing Algorithms for Imbalanced Datasets

Description Usage Arguments Details Value References Examples

Filters oversampled examples from a binary class dataset using game theory to find out if keeping an example is worthy enough.

neater(
  dataset,
  newSamples,
  k = 3,
  iterations = 100,
  smoothFactor = 1,
  classAttr = "Class"
)

`dataset`	The original `data.frame`. All columns, except `classAttr` one, have to be numeric or coercible to numeric.
`newSamples`	A `data.frame` containing the samples to be filtered. Must have the same structure as `dataset`.
`k`	Integer. Number of nearest neighbours to use in KNN algorithm to rule out samples. By default, 3.
`iterations`	Integer. Number of iterations for the algorithm. By default, 100.
`smoothFactor`	A positive `numeric`. By default, 1.
`classAttr`	`character`. Indicates the class attribute from `dataset` and `newSamples`. Must exist in them.

Uses game theory and Nash equilibriums to calculate the minority examples probability of trully belonging to the minority class. It discards examples which at the final stage of the algorithm have more probability of being a majority example than a minority one.

Filtered samples as a data.frame with same structure as newSamples.

Almogahed, B.A.; Kakadiaris, I.A. Neater: Filtering of Over-Sampled Data Using Non-Cooperative Game Theory. Soft Computing 19 (2014), Nr. 11, p. 3301–3322.

data(iris0)

newSamples <- smotefamily::SMOTE(iris0[,-5], iris0[,5])$syn_data
# SMOTE overrides Class attr turning it into class
# and dataset must have same class attribute as newSamples
names(newSamples) <- c(names(newSamples)[-5], "Class")

neater(iris0, newSamples, k = 5, iterations = 100,
       smoothFactor = 1, classAttr = "Class")