20.500.12556/DKUM-96695 A multi-objective feature selection and self-paced ensemble framework for semiconductor defect detection In semiconductor manufacturing, defect detection is commonly performed using high-dimensional process data. These data often exhibit class imbalance and class overlap, which create challenges for achieving reliable classification performance. To address these issues, this study proposes a multi-objective feature selection and self-paced ensemble (MOFS-SPE) framework. The framework employs a multi-objective evolutionary algorithm based on decomposition (MOEA/D) for feature selection. In this process, the area under the precision–recall curve (AUPRC) and the R-value are used as objective functions to identify feature subsets that are highly relevant to quality outcomes. In addition, the framework integrates the self-paced ensemble (SPE) with tree-based classifiers to handle imbalanced and overlapping data. Experiments conducted on a real semiconductor manufacturing dataset (SECOM dataset) demonstrate the effectiveness of the proposed approach. Compared with using the full feature set, the selected features increase the area under the receiver operating characteristic curve (AUROC) from 0.685 to 0.770 and the AUPRC from 0.932 to 0.972. When applying the SPE framework, the specificity of the decision tree model improves from 0.048 to 0.667, thereby enhancing the reliability of identifying defective products. Overall, the proposed framework provides a useful reference for intelligent quality inspection in semiconductor production environments. semiconductor manufacturing defect detection quality inspection class imbalance class overlap multi-objective feature selection self-paced ensemble machine learning strojno učenje iskanje napak preverjanje kakovosti true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Članek v reviji 2026-01-23 13:40:15 2026-01-23 13:40:15 2026-01-24 03:11:35 0000-00-00 00:00:00 2025 0 0 str. 458-474 no. 4 Vol. 20 Dec. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-08-27 2025-12-30 2025-12-31 658.562 265921027 10.14743/apem2025.4.552 1854-6250 APEM20-4_458-474.pdf APEM20-4_458-474.pdf 1 748D724F1D628131B3F2C195040F945E 2771abc3e66dcfa6ae7cc64dce9c065278ef1e107213501759913dee624f6e73 eaf80d95-f858-11f0-8c23-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202497 https://apem-journal.org/Archives/2025/Abstract-APEM20-4_458-474.html 0 f32b34e2-f858-11f0-8c23-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202498 Fakulteta za strojništvo Advances in production engineering & management 0 0 0