BLSMOTE: Borderline-SMOTE

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

Borderline-SMOTE

Description

Generate synthetic positive instances using Borderline-SMOTE algorithm. The number of majority neighbor of each minority instance is used to divide minority instances into 3 groups; SAFE/DANGER/NOISE, only the DANGER are used to generate synthetic instances.

Usage

BLSMOTE(X,target,K=5,C=5,dupSize=0,method =c("type1","type2"))

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, 0 for duplicating until balanced
method	A parameter to indicate which type of Borderline-SMOTE presented in the paper is used

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 determining SAFE/DANGER/NOISE
dup_size	The maximum times of synthetic minority instances over original majority instances in the oversampling
outcast	Unavailable for this method
eps	Unavailable for this method
method	The name of oversampling method and type used for this generated dataset (BLSMOTE type1/2)

Author(s)

Wacharasak Siriseriwan <[e-mail]>

References

Han, H., Wang, W.Y. and Mao, B.H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I (ICIC'05), De-Shuang Huang, Xiao-Ping Zhang, and Guang-Bin Huang (Eds.), Vol. Part I. Springer-Verlag, Berlin, Heidelberg, 2005. 878-887. DOI=http://dx.doi.org/10.1007/11538059_91

Examples

    data_example = sample_generator(5000,ratio = 0.80)
	genData = BLSMOTE(data_example[,-3],data_example[,3])
	genData_2 = BLSMOTE(data_example[,-3],data_example[,3],K=7, C=5, method = "type2")

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