Classification using class-imbalanced data is biased in favor of
the majority class. The bias is even larger for high-dimensional
data, where the number of variables greatly exceeds the number
of samples. The problem can be attenuated by undersampling or
oversampling, which produce class-balanced data. Generally
undersampling is helpful, while random oversampling is not.
Synthetic Minority Oversampling TEchnique (SMOTE) is a very
popular oversampling method that was proposed to improve random
oversampling but its behavior on high-dimensional data has not
been thoroughly investigated. In this paper we investigate the
properties of SMOTE from a theoretical and empirical point of
view, using simulated and real high-dimensional data. While in
most cases SMOTE seems beneficial with low-dimensional data, it
does not attenuate the bias towards the classification in the
majority class for most classifiers when data are high-
dimensional, and it is less effective than random undersampling.
SMOTE is beneficial for k-NN