论文标题
通过混合实验研究人工智能算法的鲁棒性
Investigating the Robustness of Artificial Intelligent Algorithms with Mixture Experiments
论文作者
论文摘要
人工智能（AI）算法（例如深度学习和XGBOOST）用于许多应用程序，包括计算机视觉，自动驾驶和医学诊断。这些AI算法的鲁棒性引起了极大的兴趣，因为不准确的预测可能会导致安全问题并限制AI系统的采用。在本文中，我们提出了一个基于实验设计的框架，以系统地研究AI分类算法的鲁棒性。在不同的应用方案下，可强大的分类算法预计将具有很高的精度和较低的可变性。鲁棒性可能会受到广泛的因素的影响，例如培训数据集中的类标签的不平衡，所选的预测算法，应用程序所选数据集以及培训和测试数据集中的分布变化。为了研究AI分类算法的鲁棒性，我们进行了一组全面的混合实验，以收集预测性能结果。然后进行统计分析以了解各种因素如何影响AI分类算法的鲁棒性。我们总结了我们的发现，并向AI应用程序中的从业者提供建议。
Artificial intelligent (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including computer vision, autonomous driving, and medical diagnostics. The robustness of these AI algorithms is of great interest as inaccurate prediction could result in safety concerns and limit the adoption of AI systems. In this paper, we propose a framework based on design of experiments to systematically investigate the robustness of AI classification algorithms. A robust classification algorithm is expected to have high accuracy and low variability under different application scenarios. The robustness can be affected by a wide range of factors such as the imbalance of class labels in the training dataset, the chosen prediction algorithm, the chosen dataset of the application, and a change of distribution in the training and test datasets. To investigate the robustness of AI classification algorithms, we conduct a comprehensive set of mixture experiments to collect prediction performance results. Then statistical analyses are conducted to understand how various factors affect the robustness of AI classification algorithms. We summarize our findings and provide suggestions to practitioners in AI applications.
