Generate synthetic positive instances using Relocating Safe-level SMOTE algorithm. Using the parameter "Safe-Level" to determine the possible location and relocating synthetic instances if there is too close to majority instances.
Usage
RSLS(X, target, K = 5, C = 5, dupSize = 0)
Value
data
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
syn_data
A set of synthetic minority instances with a vector of minority target class appended at the last column
orig_N
A set of original instances whose class is not oversampled with a vector of their target class appended at the last column
orig_P
A set of original instances whose class is oversampled with a vector of their target class appended at the last column
K
The value of parameter K for nearest neighbor process used for generating data
K_all
The value of parameter C for nearest neighbor process used for calculating safe-level
dup_size
The maximum times of synthetic minority instances over original majority instances in the oversampling
outcast
A set of original minority instances which has safe-level equal to zero and is defined as the minority outcast
eps
Unavailable for this method
method
The name of oversampling method used for this generated dataset (RSLS)
Arguments
X
A data frame or matrix of numeric-attributed dataset
target
A vector of a target class attribute corresponding to a dataset X.
K
The number of nearest neighbors during sampling process
C
The number of nearest neighbors during calculating safe-level process
dupSize
The number or vector representing the desired times of synthetic minority instances over the original number of majority instances
Siriseriwan, W. and Sinapiromsaran, K. The Effective Redistribution for Imbalance Dataset : Relocating Safe-Level SMOTE with Minority Outcast Handling. Chiang Mai Journal of Science. 43(1), 234 - 246.