ADASYN | R Documentation |
Generate synthetic positive instances using ADASYN algorithm. The number of majority neighbors of each minority instance determines the number of synthetic instances generated from the minority instance.
ADAS(X,target,K=5)
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 |
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 |
Unavailable for this method |
dup_size |
A vector of times of synthetic minority instances over original majority instances in the oversampling in each instances |
outcast |
Unavailable for this method |
eps |
Unavailable for this method |
method |
The name of oversampling method used for this generated dataset (ADASYN) |
Wacharasak Siriseriwan <wacharasak.s@gmail.com>
He, H., Bai, Y., Garcia, E. and Li, S. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference. pp.1322-1328.
data_example = sample_generator(10000,ratio = 0.80)
genData = ADAS(data_example[,-3],data_example[,3])
genData_2 = ADAS(data_example[,-3],data_example[,3],K=7)
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