标题： 评估XGBoost在平衡和不平衡数据中的表现：在欺诈检测中的应用 显示英文标题

标题： Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection

摘要： 本文评估了在不同数据集大小和类别分布情况下XGboost的性能，从完全平衡到高度不平衡。XGBoost被选为评估对象，因为它在多个基准测试中因其检测性能和速度而脱颖而出。在介绍欺诈检测问题后，本文回顾了检测系统或二分类器的评估指标，并通过示例说明不同指标在平衡和不平衡数据集上的工作方式。然后，它研究了XGBoost的原理。它提出了一种数据准备的流程，并将原始XGBoost与随机搜索调优的XGBoost进行了比较。随机搜索调优在10万样本的大数据集上提供了持续的改进，而对于10和1千样本的中等和小数据集则没有那么明显。此外，正如预期的那样，随着可用数据的增加，XGBoost的识别性能会提高，而当数据集变得更加不平衡时，检测性能会下降。对具有50、45、25和5百分比正样本分布的测试显示，检测性能的最大下降发生在只有5百分比正样本的分布上。对训练集进行采样以平衡并不提供持续的改进。因此，未来的工作将包括对不同处理数据不平衡技术的系统研究，并评估其他方法，包括图、自编码器和生成对抗方法，以应对标签不足的问题。 显示英文摘要

摘要： This paper evaluates XGboost's performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. XGBoost has been selected for evaluation, as it stands out in several benchmarks due to its detection performance and speed. After introducing the problem of fraud detection, the paper reviews evaluation metrics for detection systems or binary classifiers, and illustrates with examples how different metrics work for balanced and imbalanced datasets. Then, it examines the principles of XGBoost. It proposes a pipeline for data preparation and compares a Vanilla XGBoost against a random search-tuned XGBoost. Random search fine-tuning provides consistent improvement for large datasets of 100 thousand samples, not so for medium and small datasets of 10 and 1 thousand samples, respectively. Besides, as expected, XGBoost recognition performance improves as more data is available, and deteriorates detection performance as the datasets become more imbalanced. Tests on distributions with 50, 45, 25, and 5 percent positive samples show that the largest drop in detection performance occurs for the distribution with only 5 percent positive samples. Sampling to balance the training set does not provide consistent improvement. Therefore, future work will include a systematic study of different techniques to deal with data imbalance and evaluating other approaches, including graphs, autoencoders, and generative adversarial methods, to deal with the lack of labels.