TopSelect: A Topology-based Feature Selection Method for Industrial Machine Learning

Building robust industrial machine learning (ML) models requires incorporating domain knowledge in feature selection. This ensures building meaningful ML models that fit the context of the industrial process that consists of complex networks of thousands of elements interconnected by flows of materi...

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Veröffentlicht in:Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI S. 46 - 47
Hauptverfasser: Abukwaik, Hadil, Sula, Lefter, Rodriguez, Pablo
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 01.05.2022
Schlagworte:
Diversity reception
domain knowledge
Feature extraction
feature selection
Industrial ML
Knowledge engineering
Machine learning
Manuals
Software algorithms
topology model
Training
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Zusammenfassung:Building robust industrial machine learning (ML) models requires incorporating domain knowledge in feature selection. This ensures building meaningful ML models that fit the context of the industrial process that consists of complex networks of thousands of elements interconnected by flows of material, energy, and information. Despite the various automatic feature selection methods, they are still outperformed by the manual feature selection that embeds the industrial domain knowledge. This paper proposes an industrial feature selection method that (1) automatically captures domain knowledge from topology models holding information on the industrial plant and (2) identifies the relevant process signals (i.e., features) to a specified process element (i.e., to which an ML model is being built). We performed an empirical case study on an industrial use case to evaluate the effectiveness and efficiency of the proposed method in comparison to existing ones from literature.
DOI:10.1145/3522664.3528618