Enhanced three-stage cluster-then-classify method (ETSCCM)

Abstract

Modern steel manufacturing processes demand rigorous quality control to rapidly and accurately detect and classify defects in steel plates. In this work, we propose an enhanced three-stage cluster-then-classify method (ETSCCM) that merges clustering-based data partitioning with strategic feature subset selection and refined hyperparameter tuning. Initially, the appropriate number of clusters is determined by combining K-means with hierarchical clustering, ensuring a more precise segmentation of the Steel Plates Fault dataset. Concurrently, various correlated feature subsets are assessed to identify those that maximize classification performance. The best-performing scenario is then used in conjunction with the most effective classifier, identified through comparative analyses involving widely adopted algorithms. Experimental outcomes on real-world fault data, as well as additional publicly available datasets, indicate that our approach can achieve a significant increase in prediction accuracy compared to conventional methods. This study introduces a new method by jointly refining cluster assignments and classification parameters through scenario-based feature subsets, going beyond single-stage methods in enhancing detection accuracy. Through this multi-stage process, pivotal data relationships are uncovered, resulting in a robust, adaptable framework that advances industrial fault diagnosis.