Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm

Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm

Decision trees in machine learning achieved satisfactory performance in classification. Decision trees offer the advantage of handling high-dimensional and complexly correlated data through feature combination and selection. Extreme Gradient Boosting (XGBoost) overcomes the issue of overfitting in d...

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Bibliographic Details
Main Authors: Atul Vikas Lakra, Sudarson Jena, Kaushik Mishra
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Credit score
ensemble method
hyperparameter optimization
particle swarm optimization (PSO)
XGboost
Online Access:https://ieeexplore.ieee.org/document/11071533/
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https://ieeexplore.ieee.org/document/11071533/

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