Description Usage Arguments Value References Examples
Modification for SMOTE technique which overcomes some of the problems of the SMOTE technique when there are noisy instances, in which case SMOTE would generate more noisy instances out of them.
1 2  mwmote(dataset, numInstances, kNoisy = 5, kMajority = 3, kMinority,
threshold = 5, cmax = 2, cclustering = 3, classAttr = "Class")

dataset 

numInstances 
Integer. Number of new minority examples to generate. 
kNoisy 
Integer. Parameter of euclidean KNN to detect noisy examples as those whose whole kNoisyneighbourhood is from the opposite class. 
kMajority 
Integer. Parameter of euclidean KNN to detect majority borderline examples as those who are in any kMajorityneighbourhood of minority instances. Should be a low integer. 
kMinority 
Integer. Parameter of euclidean KNN to detect minority borderline examples as those who are in the KMinorityneighbourhood of majority borderline ones. It should be a large integer. By default if not parameter is fed to the function, S^{+}/2 where S^{+} is the set of minority examples. 
threshold 
Numeric. A positive real indicating how much we measure tolerance of closeness to the boundary of minority boundary examples. A large integer indicates more margin of distance for a example to be considerated important boundary one. 
cmax 
Numeric. A positive real indicating how much we measure tolerance of closeness to the boundary of minority boundary examples. The larger this number, the more we are valuing boundary examples. 
cclustering 
Numeric. A positive real for tuning the output of an internal clustering. The larger this parameter, the more area focused is going to be the oversampling. 
classAttr 

A data.frame
with the same structure as dataset
,
containing the generated synthetic examples.
Barua, Sukarna; Islam, Md.M.; Yao, Xin; Murase, Kazuyuki. Mwmote<e2><80><93>majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning. IEEE Transactions on Knowledge and Data Engineering 26 (2014), Nr. 2, p. 405<e2><80><93>425
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