# Class Imbalance Problem in Data Mining Review

arXiv: https://arxiv.org/abs/1305.1707 · HTML: https://mlsanity.com/paper/1305.1707/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2013.

## Built on by (most cited first, 16 of 16)

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- [DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs](https://mlsanity.com/paper/2205.10293/) (2022)
- [Smart Active Sampling to enhance Quality Assurance Efficiency](https://mlsanity.com/paper/2209.11464/) (2022)
- [Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO](https://mlsanity.com/paper/2210.12870/) (2022)
- [BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions](https://mlsanity.com/paper/2305.09777/) (2023)
- [Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning](https://mlsanity.com/paper/2402.02121/) (2024)
- [Multimodal Approaches to Fair Image Classification: An Ethical Perspective](https://mlsanity.com/paper/2412.12165/) (2024)
- [Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data](https://mlsanity.com/paper/2109.06565/) (2021)
- [A Pipeline of Augmentation and Sequence Embedding for Classification of Imbalanced Network Traffic](https://mlsanity.com/paper/2502.18909/) (2025)
- [Enhancing Imbalanced Node Classification via Curriculum-Guided Feature Learning and Three-Stage Attention Network](https://mlsanity.com/paper/2602.03808/) (2026)

## Abstract (arXiv)

In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes having more sample than other classes. The most of algorithm are more focusing on classification of major sample while ignoring or misclassifying minority sample. The minority samples are those that rarely occur but very important. There are different methods available for classification of imbalance data set which is divided into three main categories, the algorithmic approach, data-preprocessing approach and feature selection approach. Each of this technique has their own advantages and disadvantages. In this paper systematic study of each approach is define which gives the right direction for research in class imbalance problem.