Description Usage Arguments Details Value References Examples
Allows you to treat imbalanced discrete numeric datasets by generating synthetic minority examples, approximating their probability distribution.
1 
dataset 

numInstances 
Integer. Number of new minority examples to generate. 
burnin 
Integer. It determines how many examples generated for a given one are going to be discarded firstly. By default, 100. 
lag 
Integer. Number of iterations between new generated example for a minority one. By default, 20. 
classAttr 

Approximates minority distribution using Gibbs Sampler. Dataset must be
discretized and numeric. In each iteration, it builds a new sample using a
Markov chain. It discards first burnin
iterations, and from then on,
each lag
iterations, it validates the example as a new minority
example. It generates d (iterationsburnin)/lag where d is
minority examples number.
A data.frame
with the same structure as dataset
,
containing the generated synthetic examples.
Das, Barnan; Krishnan, Narayanan C.; Cook, Diane J. Racog and Wracog: Two Probabilistic Oversampling Techniques. IEEE Transactions on Knowledge and Data Engineering 27(2015), Nr. 1, p. 222<e2><80><93>234.
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