  Classification of imbalanced data is one of the common problems in the recent
field of data mining. Imbalanced data substantially affects the performance of
standard classification models. Data-level approaches mainly use the
oversampling methods to solve the problem, such as synthetic minority
oversampling Technique (SMOTE). However, since the methods such as SMOTE
generate instances by linear interpolation, synthetic data space may look like
a polygonal. Also, the oversampling methods generate outliers of the minority
class. In this paper, we proposed Gaussian based minority oversampling
technique (GMOTE) with a statistical perspective for imbalanced datasets. To
avoid linear interpolation and to consider outliers, this proposed method
generates instances by the Gaussian Mixture Model. Motivated by
clustering-based multivariate Gaussian outlier score (CMGOS), we propose to
adapt tail probability of instances through the Mahalanobis distance to
consider local outliers. The experiment was carried out on a representative set
of benchmark datasets. The performance of the GMOTE is compared with other
methods such as SMOTE. When the GMOTE is combined with classification and
regression tree (CART) or support vector machine (SVM), it shows better
accuracy and F1-Score. Experimental results demonstrate the robust performance.
