An Improved SMOTE Algorithm Based on Genetic Algorithm for Imbalanced Data Classification
GU Qiong, WANG Xian-Ming, WU Zhao, NING Bing, XIN Chun-Sheng 
Journal of Digital Information Management, Vol. 14, No. 2, 2016.
DOI: 
Link: 
Abstract:
ABSTRACT: Classification of imbalanced data has been
recognized as a crucial problem in machine learning and
data mining.In an imbalanced dataset, minority class instances
are likely to be misclassified. When the synthetic
minority over-sampling technique (SMOTE) is applied
in imbalanced dataset classification, the same sampling
rate is set for all samples of the minority class in
the process of synthesizing new samples, this scenario
involves blindness. To overcome this problem, an improved
SMOTE algorithm based on genetic algorithm (GA),
namely, GASMOTE was proposed. First, GASMOTE set
different sampling rates for different minority class
samples. A combination of the sampling rates corresponded
to an individual in the population. Second, the
selection, crossover, and mutation operators of GA were
iteratively applied to the population to obtain the best
combination of sampling rates when the stopping criteria
were met. Lastly, the best combination of sampling rates
was used in SMOTE to synthetize new samples. Experimental
results on 10 typical imbalanced datasets show
that GASMOTE increases the F-measure value by 5.9%
and the G-mean value by 1.6% compared with the SMOTE
algorithm. Meanwhile, GASMOTE increases the F-measure
value by 3.7% and the G-mean value by 2.3% compared
with the borderline-SMOTE algorithm. GASMOTE
can be utilized as a new over-sampling technique to address
the problem of imbalanced dataset classification.