Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding

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