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Classification of imbalanced data is one of the common problems in the recentfield of data mining. Imbalanced data substantially affects the performance ofstandard classification models. Data-level approaches mainly use theoversampling methods to solve the problem, such as synthetic minorityoversampling Technique (SMOTE). However, since the methods such as SMOTEgenerate instances by linear interpolation, synthetic data space may look likea polygonal. Also, the oversampling methods generate outliers of the minorityclass. In this paper, we proposed Gaussian based minority oversamplingtechnique (GMOTE) with a statistical perspective for imbalanced datasets. Toavoid linear interpolation and to consider outliers, this proposed methodgenerates instances by the Gaussian Mixture Model. Motivated byclustering-based multivariate Gaussian outlier score (CMGOS), we propose toadapt tail probability of instances through the Mahalanobis distance toconsider local outliers. The experiment was carried out on a representative setof benchmark datasets. The performance of the GMOTE is compared with othermethods such as SMOTE. When the GMOTE is combined with classification andregression tree (CART) or support vector machine (SVM), it shows betteraccuracy and F1-Score. Experimental results demonstrate the robust performance.

GMOTE: Gaussian based minority oversampling technique for imbalanced classification adapting tail probability of outliers