传统统计方法在复杂系统可靠性分析中比机器学习更精准可解释。
Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning?
- 用经典统计模型对比机器学习方法进行可靠性建模
- 实测与模拟数据均显示统计方法精度更高
- 适合注重可解释性的工程可靠性场景
使用机器学习技术研究复杂系统的可靠性面临一系列技术和实际挑战，涵盖系统和数据的内在特性、建模难度以及在真实场景中有效部署模型的困难。本研究比较了经典统计技术与机器学习方法在提升复杂系统可靠性评估中的有效性。结果表明，在许多实际应用中，经典统计算法通常比黑箱机器学习方法产生更精确且可解释的结果。评估基于真实世界数据和模拟场景进行，涵盖了统计建模算法，以及神经网络、K近邻和随机森林等机器学习方法的性能表现。
原文摘要 · Abstract (English)
Studying the reliability of complex systems using machine learning techniques involves facing a series of technical and practical challenges, ranging from the intrinsic nature of the system and data to the difficulties in modeling and effectively deploying models in real-world scenarios. This study compares the effectiveness of classical statistical techniques and machine learning methods for improving complex system analysis in reliability assessments. We aim to demonstrate that classical statistical algorithms often yield more precise and interpretable results than black-box machine learning approaches in many practical applications. The evaluation is conducted using both real-world data and simulated scenarios. We report the results obtained from statistical modeling algorithms, as well as from machine learning methods including neural networks, K-nearest neighbors, and random forests.
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