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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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用遗传算法和XGBoost进行超参数优化:在智能电网欺诈检测方面迈出了一步.

Adil Mehdary1, Abdellah Chehri2, Abdeslam Jakimi3

  • 1LaGes, Hassania School of Public Works, Casablanca 20000, Morocco.

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概括

遗传算法 (GA) 优化了XGBoost用于智能电网欺诈检测,显著提高了准确度从0.82到0.978. 这提高了在智能电网中检测欺诈活动的效率和可靠性.

关键词:
美国SGCC数据集在XGBoost中使用.电力盗窃 电力盗窃 电力盗窃发现欺诈 发现欺诈遗传算法 遗传算法超参数优化超参数优化听算法 (Metaheuristic Algorithms) 是一种算法,可以通过智能电网是一个智能电网.

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 数据科学数据科学数据科学

背景情况:

  • 智能电网越来越容易受到复杂的欺诈.
  • 现有的欺诈检测模型往往需要显著的性能调整.
  • 超参数优化对于机器学习模型的有效性至关重要.

研究的目的:

  • 优化XGBoost模型,以提高智能电网中的欺诈检测.
  • 为了评估遗传算法 (GA) 对XGBoost超参数调整的影响.
  • 提高智能电网欺诈检测系统的准确性和效率.

主要方法:

  • 利用遗传算法 (GA) 来进行XGBoost模型的超参数优化.
  • 将优化的XGBoost模型应用于智能电网欺诈检测数据集.
  • 使用准确性,精度,回忆和AUROC等指标评估模型性能.

主要成果:

  • 在优化后,模型准确度从0.82增加到0.978.
  • 在精度,回忆和AUROC指标方面显著改进.
  • 在这种情况下,验证了GA驱动的XGBoost超参数调整的有效性.

结论:

  • 基因算法和XGBoost的结合为智能电网欺诈检测提供了一个强大的方法.
  • 优化的XGBoost模型在识别欺诈活动方面表现出卓越的性能.
  • 这项研究有助于推进智能电网基础设施的安全性和完整性.