SLS: Safe-level SMOTE

In smotefamily: A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE

Safe-level SMOTE

Description

Generate synthetic positive instances using Safe-level SMOTE algorithm. Using the parameter "Safe-level" to determine the possible location of synthetic instances.

Usage

SLS(X, target, K = 5, C = 5, dupSize = 0)

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

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 (SLS)

Author(s)

Wacharasak Siriseriwan <[e-mail]>

References

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.

Examples

    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)

smotefamily documentation built on May 29, 2024, 7:54 a.m.