Generate synthetic positive instances using Safe-level SMOTE algorithm. Using the parameter "Safe-level" to determine the possible location of synthetic instances.
SLS(X, target, K = 5, C = 5, 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
The value of parameter K for nearest neighbor process used for generating data
The value of parameter C for nearest neighbor process used for calculating safe-level
The maximum times of synthetic minority instances over original majority instances in the oversampling
A set of original minority instances which has safe-level equal to zero and is defined as the minority outcast
Unavailable for this method
The name of oversampling method used for this generated dataset (SLS)
A data frame or matrix of numeric-attributed dataset
A vector of a target class attribute corresponding to a dataset X.
The number of nearest neighbors during sampling process
The number of nearest neighbors during calculating safe-level process
The number or vector representing the desired times of synthetic minority instances over the original number of majority instances
Wacharasak Siriseriwan <wacharasak.s@gmail.com>
Bunkhumpornpat, C., Sinapiromsaran, K. and Lursinsap, C. 2009. Safe-level-SMOTE: Safe-level-synthetic minority oversampling technique for handling the class imbalanced problem. Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 2009, 475-482.
data_example = sample_generator(5000,ratio = 0.80)
genData = SLS(data_example[,-3],data_example[,3])
genData_2 = SLS(data_example[,-3],data_example[,3],K=7, C=5)
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