Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding
- 1 January 2006
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
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 SMOTEKeywords
This publication has 3 references indexed in Scilit:
- Local Fisher embeddingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- SMOTE: Synthetic Minority Over-sampling TechniqueJournal of Artificial Intelligence Research, 2002
- Nonlinear Dimensionality Reduction by Locally Linear EmbeddingScience, 2000