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.
1 2  neater(dataset, newSamples, k = 3, iterations = 100, smoothFactor = 1,
classAttr = "Class")

dataset 
The original 
newSamples 
A 
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 
classAttr 

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 OverSampled Data Using NonCooperative Game Theory. Soft Computing 19 (2014), Nr. 11, p. 3301<e2><80><93>3322.
1 2 3 4 5 6 7 8 9  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")

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