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

• URL: http://arxiv.org/abs/2303.04486v1
• Date: Wed, 8 Mar 2023 10:19:55 GMT
• Title: Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets
• Authors: S.C. Bee, E. Papatheou, M Haywood-Alexander, R.S. Mills, L.A. Bull, K. Worden and N. Dervilis
• Abstract summary: This paper presents the use of multi-task learning (MTL) 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 MTL results were interpretable, highlighting structural differences as opposed to differences in experimental set-up.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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