PDF(Pubmed)
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.
摘要：
本研究对遗传算法(GA)和XGBoost的组合进行了全面分析,一个著名的机器学习模型。主要重点在于智能电网应用中欺诈检测的超参数优化。实证结果表明，优化后模型的性能指标有了值得注意的提高，特别强调精度从0.82大幅提高到0.978。精度，召回，AUROC指标显示出明显的改善，表明优化XGBoost模型进行欺诈检测的有效性。我们的研究结果为扩展智能电网欺诈检测领域做出了重要贡献。这些结果强调了高级元启发式算法用于优化复杂机器学习模型的潜在用途。这项工作展示了在提高智能电网中欺诈检测系统的准确性和效率方面的重大进展。
