Feature Selection to Address High Dimensionality in Industry 4.0 Multi-Emitter Laser Modules Assembly Lines

Industry 4.0 has increased data depth and breadth in high-tech manufacturing, but high dimensionality and sparsity persist. High-dimensional space’s sparsity makes classical learning and knowledge extraction algorithms ineffective and error-prone. Dimension reduction methods like feature selection (FS) seem to address this problem. This study addresses these challenges by conducting a comparative analysis on a real laser assembly industrial case of high dimensions. We explore five stand-alone methods, NCFS, RReliefF, minimum redundancy maximum relevance (MRMR), recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) [applied to datasets from two laser modules (d-serie and s-serie)]. Additionally, two hybrid methods, RReliefF-RFE and MRMR-RFE, are evaluated, broadening the scope of FS strategies. Time efficiency prioritizes RReliefF, NCFS, and LASSO, while RReliefF-RFE, NCFS, and LASSO excel in interpretability, achieving significant predictor reduction without compromising accuracy. The study thus provides insights into the selection of FS methods in a challenging industrial laser assembly setting.