Complex product quality prediction based on ResNeSt model combining class-aware attention mechanism and dynamically updating class weights

Abstract The processing state data of complex products often exhibits high dimensionality, intricate feature relationships, and imbalanced distributions, hindering the accuracy of data-driven quality prediction models. To address these challenges, this paper proposes a complex product quality prediction model that integrates a class-aware attention mechanism and dynamic class weight update strategy. To handle the high dimensionality and complex correlations of the data, Pearson Correlation Coefficient and Gradient Boosting Decision Tree-based Recursive Feature Elimination (GBDT-RFE) algorithms are employed for feature selection. In response to the imbalanced distribution of data categories, a class-aware attention mechanism module is built upon the ResNeSt network, utilizing prior knowledge of data distribution to adjust the model's focus. Furthermore, a loss function is designed to dynamically update class weights based on classification error rates, enabling the model to adaptively adjust the weight allocation for different classes, thereby enhancing its generalization capability. Experimental results on a semiconductor industry dataset demonstrate that the proposed model outperforms the original ResNeSt model in multiple metrics, with accuracy improved by 3.5%, AUC increased by 12.6%, F1 score raised by 18.9%, and recall enhanced by 24%, ultimately achieving an overall prediction accuracy of 98.7%. These multidimensional improvements make the model suitable for various complex product data scenarios such as electronics manufacturing, automotive industry, and biopharmaceuticals, demonstrating broad applicability.