0th

Percentile

Adaptive Synthetic Sampling Approach for Imbalanced Learning

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.

Keywords
manip , methods
Usage
ADAS(X,target,K=5)
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

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

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)

References

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.

Aliases
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