Abstract
The classification of imbalanced data is a common
practice in the context of medical imaging intelligence.
The synthetic minority oversampling technique
(SMOTE) is a powerful approach to tackling the
operational problem. This paper presents a novel
approach to improving the conventional SMOTE
algorithm by incorporating the locally linear
embedding algorithm (LLE). The LLE algorithm is
first applied to map the high-dimensional data into a
low-dimensional space, where the input data is more
separable, and thus can be oversampled by SMOTE.
Then the synthetic data points generated by SMOTE
are mapped back to the original input space as well
through the LLE. Experimental results demonstrate
that the underlying approach attains a performance
superior to that of the traditional SMOTE.