Oversampling Classification of Multi-class Imbalanced Data based on Metaheuristic Algorithm

Xin ji Chen; Jing Lun Zhu; Ze Yu Liu; Jian wei Liu

doi:10.1504/ijbic.2025.10077635

January 1, 2025International Journal of Bio-Inspired Computation

Oversampling Classification of Multi-class Imbalanced Data based on Metaheuristic Algorithm

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim is to explore how metaheuristic algorithms can improve oversampling methods for classifying multi-class imbalanced data.
Application of metaheuristic algorithms for data oversampling.
Implementation of classification techniques on generated datasets.
Evaluation of algorithm performance through classification accuracy measurements.
Improved classification accuracy with oversampling techniques.
Enhanced performance metrics for multi-class data compared to traditional methods.

Implication

This work demonstrates oversampling techniques to enhance classification accuracy in multi-class imbalanced data, highlighting improvements in algorithm performance.

Abstract

No abstract yet. The journal hasn't published one we can show.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/69ddda0de195c95cdefd77d7 https://doi.org/10.1504/ijbic.2025.10077635

View Full Paper

Ask AI

Bookmark

Share

Oversampling Classification of Multi-class Imbalanced Data based on Metaheuristic Algorithm

Key Points

Implication

Abstract

Discussion

Cite This Study

Oversampling Classification of Multi-class Imbalanced Data based on Metaheuristic Algorithm

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading