# An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification

arXiv: https://arxiv.org/abs/2109.00201 · HTML: https://mlsanity.com/paper/2109.00201/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 79 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Generative Adversarial Minority Oversampling](https://mlsanity.com/paper/1903.09730/) (2019)
- [Imbalanced Deep Learning by Minority Class Incremental Rectification](https://mlsanity.com/paper/1804.10851/) (2018)
- [Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification](https://mlsanity.com/paper/1804.07155/) (2018)

## Built on by (most cited first, 5 of 5)

- [Breast Cancer Classification with Enhanced Interpretability: DALAResNet50 and DT Grad-CAM](https://mlsanity.com/paper/2308.13150/) (2023)
- [Detecting broken Absorber Tubes in CSP plants using intelligent sampling and dual loss](https://mlsanity.com/paper/2211.14077/) (2022)
- [Feature Selection via Robust Weighted Score for High Dimensional Binary Class-Imbalanced Gene Expression Data](https://mlsanity.com/paper/2401.12667/) (2024)
- [Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models](https://mlsanity.com/paper/2211.00731/) (2022)
- [Crash Severity Risk Modeling Strategies under Data Imbalance](https://mlsanity.com/paper/2412.02094/) (2024)

## Abstract (arXiv)

In predictive tasks, real-world datasets often present different degrees of imbalanced (i.e., long-tailed or skewed) distributions. While the majority (the head) classes have sufficient samples, the minority (the tail) classes can be under-represented by a rather limited number of samples. Data pre-processing has been shown to be very effective in dealing with such problems. On one hand, data re-sampling is a common approach to tackling class imbalance. On the other hand, dimension reduction, which reduces the feature space, is a conventional technique for reducing noise and inconsistencies in a dataset. However, the possible synergy between feature selection and data re-sampling for high-performance imbalance classification has rarely been investigated before. To address this issue, we carry out a comprehensive empirical study on the joint influence of feature selection and re-sampling on two-class imbalance classification. Specifically, we study the performance of two opposite pipelines for imbalance classification by applying feature selection before or after data re-sampling. We conduct a large number of experiments, with a total of 9225 tests, on 52 publicly available datasets, using 9 feature selection methods, 6 re-sampling approaches for class imbalance learning, and 3 well-known classification algorithms. Experimental results show that there is no constant winner between the two pipelines; thus both of them should be considered to derive the best performing model for imbalance classification. We find that the performance of an imbalance classification model not only depends on the classifier adopted and the ratio between the number of majority and minority samples, but also depends on the ratio between the number of samples and features. Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.