Cost-informed dimensionality reduction for structural digital twin technologies

TL;DR

This paper introduces a cost-aware, decision-theoretic method for dimensionality reduction in classification models for structural digital twins, aiming to minimize misclassification costs while reducing feature space complexity.

Contribution

It formulates a novel eigenvalue-based approach that incorporates misclassification costs into the dimensionality reduction process for asset management models.

Findings

Effective reduction of feature space with minimal increase in misclassification costs

Demonstrated approach on synthetic case study shows promising results

Provides a decision-theoretic framework for cost-sensitive dimensionality reduction

Abstract

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.