Product quality prediction based on integrated feature selection

Product quality prediction models based on big data and artificial intelligence technologies can quickly and accurately analyze massive amounts of data to ensure that products meet higher quality standards. However, in the process of data fitting, how to screen data sets with linear and nonlinear characteristics to improve the accuracy of the prediction model is an urgent problem to be solved. Therefore, it is necessary to carry out research on data feature screening in product quality prediction models. We construct a prediction model based on the Stacking structure and adopted an integrated learning approach for feature screening considering the diversity of data. In this paper, firstly, we briefly summarize the process of the product processing quality prediction problem; secondly, we introduce the data acquisition and preprocessing in this chapter; then, we focus on the improved support vector regression and Radio Free Ethernet (SVR-RFE) integrated feature selection algorithm for feature screening and construct a model including Convolutional Neural Network, Bi-directional Long Short-Term Memory, and Multi Head Self Attention (CNN-BiLSTM-SE attention) under the integrated feature selection; finally, we put the constructed model into a real case for validation to confirm the Finally, the constructed model was validated in real cases to confirm the feasibility and effectiveness of the constructed model.