Paper Type
Short
Paper Number
PACIS2025-1073
Description
Imbalanced datasets are common in many real-world applications, where the number of instances in one class is significantly lower than the other. This can lead to poor performance of machine learning models, as they tend to favor the majority class. In this paper, we propose a novel approach to balance imbalanced datasets that consider both class imbalance and model fairness and cost. Our method utilizes a customized crowding distance measure to achieve a balance between classes while ensuring the model is fair to all groups
Recommended Citation
Sengewald, Julian and Lackes, Richard, "A Tailored NSGA-II Approach for Responsible Managerial ML Optimization on Imbalanced Data" (2025). PACIS 2025 Proceedings. 19.
https://aisel.aisnet.org/pacis2025/general_topic/general_topic/19
A Tailored NSGA-II Approach for Responsible Managerial ML Optimization on Imbalanced Data
Imbalanced datasets are common in many real-world applications, where the number of instances in one class is significantly lower than the other. This can lead to poor performance of machine learning models, as they tend to favor the majority class. In this paper, we propose a novel approach to balance imbalanced datasets that consider both class imbalance and model fairness and cost. Our method utilizes a customized crowding distance measure to achieve a balance between classes while ensuring the model is fair to all groups
Comments
General