ArticleSensors (Basel, Switzerland)2024
Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 63 citations in OpenAlex.
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- The crucial role of machine learning models in predicting current childhood asthma: model comparison, calibration, and SHAP-based interpretation.Frontiers in public health · 2026Article
- Early prediction of antibiotic need and bacteremia risk in non-immunocompromised pediatric emergency patients using machine learning.Pediatric research · 2025Article
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- Structurally informed models of directed brain connectivity.Nature reviews. Neuroscience · 2025Review
- Optimizing hyperparameters of YOLOv10 for arson detection using advanced optimization algorithms.PloS one · 2025Article
- Inferring building height from footprint morphology data.Scientific reports · 2024Article
- Development of an advanced maternal health risk detection system using enhanced XGBOOST and blending models.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model's performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.