Neighbor displacement-based enhanced synthetic oversampling for multiclass imbalanced data

TL;DR

This paper introduces NDESO, a hybrid oversampling method that improves multiclass imbalanced data classification by relocating noisy data points closer to their neighbors before oversampling, leading to better model performance.

Contribution

The paper proposes NDESO, a novel neighbor displacement-based oversampling technique that enhances synthetic data generation for imbalanced multiclass datasets, outperforming existing methods.

Findings

NDESO outperforms 14 alternatives in G-mean scores.

The method achieves the lowest statistical mean rank.

Extensive evaluations on synthetic and real datasets validate its effectiveness.

Abstract

Imbalanced multiclass datasets pose challenges for machine learning algorithms. These datasets often contain minority classes that are important for accurate prediction. Existing methods still suffer from sparse data and may not accurately represent the original data patterns, leading to noise and poor model performance. A hybrid method called Neighbor Displacement-based Enhanced Synthetic Oversampling (NDESO) is proposed in this paper. This approach uses a displacement strategy for noisy data points, computing the average distance to their neighbors and moving them closer to their centroids. Random oversampling is then performed to achieve dataset balance. Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasets with varying imbalance ratios. The results show that our method outperforms its competitors regarding average G-mean score and achieves the lowest statistical mean rank. This highlights its superiority and suitability for addressing data imbalance in practical applications.