# ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets

arXiv: https://arxiv.org/abs/2203.10769 · HTML: https://mlsanity.com/paper/2203.10769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 51 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Gradient Harmonized Single-stage Detector](https://mlsanity.com/paper/1811.05181/) (2018)
- [Self-paced Ensemble for Highly Imbalanced Massive Data Classification](https://mlsanity.com/paper/1909.03500/) (2019)
- [MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler](https://mlsanity.com/paper/2010.08830/) (2020)

## Abstract (arXiv)

Nowadays, many classification algorithms have been applied to various industries to help them work out their problems met in real-life scenarios. However, in many binary classification tasks, samples in the minority class only make up a small part of all instances, which leads to the datasets we get usually suffer from high imbalance ratio. Existing models sometimes treat minority classes as noise or ignore them as outliers encountering data skewing. In order to solve this problem, we propose a bagging ensemble learning framework ASE (Anomaly Scoring Based Ensemble Learning). This framework has a scoring system based on anomaly detection algorithms which can guide the resampling strategy by divided samples in the majority class into subspaces. Then specific number of instances will be under-sampled from each subspace to construct subsets by combining with the minority class. And we calculate the weights of base classifiers trained by the subsets according to the classification result of the anomaly detection model and the statistics of the subspaces. Experiments have been conducted which show that our ensemble learning model can dramatically improve the performance of base classifiers and is more efficient than other existing methods under a wide range of imbalance ratio, data scale and data dimension. ASE can be combined with various classifiers and every part of our framework has been proved to be reasonable and necessary.