SAFE-IML: Sparsity-Aware Feature Extraction for Interpretable Machine Learning with two-stage neural network modelling

Wei, H.-L. orcid.org/0000-0002-4704-7346 (Accepted: 2025) SAFE-IML: Sparsity-Aware Feature Extraction for Interpretable Machine Learning with two-stage neural network modelling. In: 2025 10th International Conference on Machine Learning Technologies (ICMLT 2025). 2025 10th International Conference on Machine Learning Technologies (ICMLT 2025), 23-25 May 2025, Helsinki, Finland. Institute of Electrical and Electronics Engineers (IEEE) (In Press)

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2025 The Author(s).

Keywords: machine learning; model interpretability; feature engineering; feature selection; neural network; sparse modelling
Dates:
  • Accepted: 14 May 2025
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering
Funding Information:
Funder
Grant number
NATURAL ENVIRONMENT RESEARCH COUNCIL
NE/W005875/1
SCIENCE AND TECHNOLOGY FACILITIES COUNCIL
ST/Y001524/1
NATURAL ENVIRONMENT RESEARCH COUNCIL
NE/V001787/1
NATURAL ENVIRONMENT RESEARCH COUNCIL
APP3762 NE/Y503290/1
Depositing User: Symplectic Sheffield
Date Deposited: 06 Aug 2025 08:12
Last Modified: 06 Aug 2025 08:19
Status: In Press
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Related URLs:
Open Archives Initiative ID (OAI ID):

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