Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features
Electrospinning is a highly sensitive fabrication process in which small variations in operating parameters can significantly influence fiber morphology and material performance. Machine learning (ML) methods are increasingly employed to model these process-structure relationships and to identify the relative importance of processing variables. However, most existing studies rely on a single ML model, implicitly assuming that the resulting feature importance is robust and reproducible. In this study, the consistency of feature importance across multiple ML model families was systematically evaluated using a curated dataset of 96 polyvinyl alcohol (PVA) electrospinning experiments. Twenty-...
| Authors | Mehrab Mahdian, Ferenc Ender, Tamas Pardy |
|---|---|
| Primary category | cs.LG |
| Published | 6 May 2026 |
| Last updated at source | 12 May 2026 |
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