Generate a oversampling dataset from imbalanced dataset using Adaptive Neighbor SMOTE which provides the parameter K to each minority instance automatically
ANS(X, target, dupSize = 0)
A resulting dataset consists of original minority instances, synthetic minority instances and original majority instances with a vector of their respective target class appended at the last column
A set of synthetic minority instances with a vector of minority target class appended at the last column
A set of original instances whose class is not oversampled with a vector of their target class appended at the last column
A set of original instances whose class is oversampled with a vector of their target class appended at the last column
A vector of parameter K for each minority instance
The value of parameter C for nearest neighbor process used for identifying outcasts
The maximum times of synthetic minority instances over original majority instances in the oversampling
A set of original minority instances which is defined as minority outcast
The value of eps which determines automatic K
The name of oversampling method used for this generated dataset (ANS)
A data frame or matrix of numeric-attributed dataset
A vector of a target class attribute corresponding to a dataset X.
A number of vector representing the desired times of synthetic minority instances over the original number of majority instances, 0 for balanced dataset.
Wacharasak Siriseriwan <wacharasak.s@gmail.com>
Siriseriwan, W. and Sinapiromsaran, K. Adaptive neighbor Synthetic Minority Oversampling TEchnique under 1NN outcast handling.Songklanakarin Journal of Science and Technology.
data_example = sample_generator(5000,ratio = 0.80)
genData = ANS(data_example[,-3],data_example[,3])
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