Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets

2023/03/08 by Sarah Bee, Bee, S. C., E. Papatheou +11
Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Structural Health Monitoring Techniques

paper · pdf · doi:10.48550/arxiv.2303.04486

openalex publication_date 2023/03/08 · openalex created_date 2023/03/10 · openalex updated_date 2026/07/28

Abstract

There have been recent efforts to move to population-based structural health monitoring (PBSHM) systems. One area of PBSHM which has been recognised for potential development is the use of multi-task learning (MTL); algorithms which differ from traditional independent learning algorithms. Presented here is the use of the MTL, ''Joint Feature Selection with LASSO'', to provide automatic feature selection for a structural dataset. The classification task is to differentiate between the port and starboard side of a tailplane, for samples from two aircraft of the same model. The independent learner produced perfect F1 scores but had poor engineering insight; whereas the MTL results were interpretable, highlighting structural differences as opposed to differences in experimental set-up.

Related

• When is an SHM problem a Multi-Task-Learning problem?
• MTLComb: multi-task learning combining regression and classification tasks for joint feature selection
• Active learning for semi-supervised structural health monitoring
• LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm
• Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras