# A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios

arXiv: https://arxiv.org/abs/2103.13823 · HTML: https://mlsanity.com/paper/2103.13823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 46 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Online Ensemble Learning for Imbalanced Data Streams](https://mlsanity.com/paper/1310.8004/) (2013)

## Built on by (most cited first, 1 of 1)

- [An Asymmetric Contrastive Loss for Handling Imbalanced Datasets](https://mlsanity.com/paper/2207.07080/) (2022)

## Abstract (arXiv)

Imbalance in the proportion of training samples belonging to different classes often poses performance degradation of conventional classifiers. This is primarily due to the tendency of the classifier to be biased towards the majority classes in the imbalanced dataset. In this paper, we propose a novel three step technique to address imbalanced data. As a first step we significantly oversample the minority class distribution by employing the traditional Synthetic Minority OverSampling Technique (SMOTE) algorithm using the neighborhood of the minority class samples and in the next step we partition the generated samples using a Gaussian-Mixture Model based clustering algorithm. In the final step synthetic data samples are chosen based on the weight associated with the cluster, the weight itself being determined by the distribution of the majority class samples. Extensive experiments on several standard datasets from diverse domains shows the usefulness of the proposed technique in comparison with the original SMOTE and its state-of-the-art variants algorithms.