Multi-Imbalance: An open-source software for multi-class imbalance learning

By Chongsheng Zhang, Jingjun Bi, Shixin Xu, Enislay Ramentol, Gaojuan Fan, Baojun Qiao, Hamido Fujita. 2019

162 citations recorded by the source corpus.

Abstract Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class imbalance classification are relatively mature nowadays, yet multi-class imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field. In this paper, we present Multi-Imbalance, an open source software package for multi-class imbalanced data classification. It provides users with seven different categories of multi-class imbalance learning algorithms, including the latest advances in the field. The source codes and documentations for Multi-Imbalance are publicly available at https://github.com/chongshengzhang/Multi_Imbalance .

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xi Xiao
2. Bowen Zhao
3. Bin Zhang
4. Shutao Xia
5. Guojun Gan
6. Han-Jia Ye
7. De-chuan Zhan
8. Da-Wei Zhou

Citations in the corpus

• Maintaining Discrimination and Fairness in Class Incremental Learning (2019, background): “Though class imbalance is an old topic and has attracted a lot of attention [4, 15, 17], multi-class imbalance learning is still an open problem [35].”