Abstract
Unbalanced classification is an essential machine learning task, which has attracted widespread attention from both the academic and industrial communities due mainly to its broad applications. Evolutionary computation (EC) has contributed greatly to unbalanced classification. However, to the best of our knowledge, there have not been any comprehensive investigations on the strengths and weaknesses of alternative EC methods in addressing various challenging problems in unbalanced classification. This paper reviews the literature which utilize EC techniques for unbalanced classification, with the aim of revealing the contributions of EC to unbalanced classification, providing an overview of recent advances, and identifying limitations of existing works. In addition, we present a series of real-world applications, and identify open challenges as well as possible research directions for the future. IEEE
| Original language | English |
|---|---|
| Pages (from-to) | 1-1 |
| Number of pages | 1 |
| Journal | IEEE Transactions on Evolutionary Computation |
| DOIs | |
| Publication status | Published - 2023 |
| Externally published | Yes |
Funding
No Statement Available
Keywords
- Cancer
- Class imbalance
- Computer science
- Costs
- Evolutionary computation
- Evolutionary Computation
- Machine Learning
- Sampling methods
- Task analysis
- Training
- Unbalanced classification
