A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?

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 article 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.

• Publication year

  2024

• Venue

  IEEE Transactions on Evolutionary Computation

• Publication date

  2024-04-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/TEVC.2023.3257230
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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