## 5 Conclusions  
This study proposes a novel framework for the prediction of strip steel defects based on a stacking ensemble approach. The framework addresses the issues of class imbalance and overfitting, thereby achieving accurate prediction outcomes. The model framework consists of three key components, which collectively enhance prediction performance.  
Firstly, the strategy of K-means++ clustering combined with density estimation was employed to enhance the Smote algorithm. These enhancements significantly reduced the production of noise and provided more classification information. At the data processing stage, the ISmote algorithm was utilised to address the issue of data imbalance.  
Secondly, a dynamic parameter adjustment combined with a mutation strategy was employed to enhance the IPSO algorithm. These enhancements facilitated the convergence of the IPSO algorithm, while also preserving the diversity of the solution space, thus mitigating the premature convergence issue commonly encountered in complex optimisation tasks.  
Finally, GBDT, XGBoost, CatBoost, and LightGBM were selected to construct a strip steel defect prediction model based on the stacking approach. The IIPSO algorithm was employed for hyperparameter optimisation with the objective of enhancing the model's prediction accuracy and generalisation ability.  
The experimental results demonstrate that the proposed model framework exhibits high accuracy and feasibility for application in the prediction of defects in strip steel. Furthermore, it can be concluded that the IIPSO and ISmote algorithms enhanced the performance of the model. Concurrently, the model framework exhibits a high predictive performance for the categories Defect 1, Defect 3, and Defect 5. This implies that the detection outcomes for these defects are more dependable, thereby reducing misjudgements and omissions and enhancing the overall detection accuracy. In conclusion, the proposed  
Figure 14: ROC curves of the IIPSO_ISmote_Stacking model.  
Figure 15: Identified strip steel defects: (**a**) Defect 1; (**b**) Defect 3; and (**c**) Defect 5.  
framework represents a novel solution for similar classification problems. Further work could be conducted to examine the potential for additional algorithmic enhancements and to extend the proposed method to a more diverse range of application domains.  
The process of feature selection was not carried out in this study. Nevertheless, it is evident that feature selection techniques are of significant value in the removal of superfluous information and the refinement of pivotal features, thereby optimising the performance of the model. In subsequent studies, a range of feature selection techniques may be employed in order to enhance model performance by reducing the feature set.  
Conceptualization and methodology, Z.F.; data curation and formal analysis, Z.F.; experiments and analysis, Z.F.; investigation, Z.F.; validation and visualization, Z.F.; writing--original draft preparation, Z.F.; writing--review and editing, Z.F. and S.Y.; resources and supervision, F.Z.; funding acquisition, S.Y. and B.W. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the National Science and Technology Program during the Twelfth Five-year Plan Period (2015 BAF10B00) and the Science and Technology Innovation Plan of Shanghai Science and Technology Commission (17511110204).  
Not applicable.  
The dataset utilised in this study, which concerns strip defects, was sourced from the University of California, Irvine (UCI) machine learning repository.  
The authors declare no conflicts of interest.