  This paper proposes a new RWO-Sampling (Random Walk Over-Sampling) based on
graphs for imbalanced datasets. In this method, two schemes based on
under-sampling and over-sampling methods are introduced to keep the proximity
information robust to noises and outliers. After constructing the first graph
on minority class, RWO-Sampling will be implemented on selected samples, and
the rest will remain unchanged. The second graph is constructed for the
majority class, and the samples in a low-density area (outliers) are removed.
Finally, in the proposed method, samples of the majority class in a
high-density area are selected, and the rest are eliminated. Furthermore,
utilizing RWO-sampling, the boundary of minority class is increased though the
outliers are not raised. This method is tested, and the number of evaluation
measures is compared to previous methods on nine continuous attribute datasets
with different over-sampling rates and one data set for the diagnosis of
COVID-19 disease. The experimental results indicated the high efficiency and
flexibility of the proposed method for the classification of imbalanced data
