根据误判成本优化降维，提升结构资产管理模型精度。
Cost-informed dimensionality reduction for structural digital twin technologies
- 基于决策理论构建加权降维方法，考虑误判代价
- 将分类可分性按误判成本加权，转化为特征值问题
- 适合需权衡成本与精度的工程资产管理场景
分类模型是支持资产决策的结构数字孪生技术中的关键组件。在构建分类模型时，输入特征空间的维度是一个重要考量。若维度过高，可能引发“维度灾难”，表现为预测性能下降。为缓解此问题，可采用降维技术。本文提出一种面向结构资产管理的决策理论型降维方法，目标是在降低维度的同时最小化误判成本，因降维可能导致判别信息丢失。该方法将类别间可分性按误判成本加权，构建为一个特征值问题。通过一个合成案例研究验证了该方法的有效性。
原文摘要 · Abstract (English)
Classification models are a key component of structural digital twin technologies used for supporting asset management decision-making. An important consideration when developing classification models is the dimensionality of the input, or feature space, used. If the dimensionality is too high, then the `curse of dimensionality' may rear its ugly head; manifesting as reduced predictive performance. To mitigate such effects, practitioners can employ dimensionality reduction techniques. The current paper formulates a decision-theoretic approach to dimensionality reduction for structural asset management. In this approach, the aim is to keep incurred misclassification costs to a minimum, as the dimensionality is reduced and discriminatory information may be lost. This formulation is constructed as an eigenvalue problem, with separabilities between classes weighted according to the cost of misclassifying them when considered in the context of a decision process. The approach is demonstrated using a synthetic case study.
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