Research on the application of Bagging W-KNN algorithm in alloy steel identification with PXRF analyzer

In response to the inadequate alloy steel identification capability and intelligent recognition of domestic portable X-ray fluorescence analyzers, as well as the impact of existing classification algorithms due to factors such as imbalanced data distribution, this paper proposes the Bagging W-KNN intelligent identification algorithm for alloy steel grade. This method is based on the K-nearest neighbor classification algorithm, employs the Chi-Square statistical method, Bagging ensemble algorithm, introduces the Gini index from the decision tree classification algorithm, and utilizes the Gaussian function probability estimation method. Comparative experiments with four typical algorithms—Decision Tree, KNN, SVM, and Naive Bayes—demonstrate that the proposed algorithm achieves accuracy, precision, recall, and F1 values of 97.01%, 97.30%, 96.69%, and 96.18% respectively, significantly outperforming the other four algorithms and improving prediction accuracy. •We propose an optimization of KNN algorithm and implement a new algorithm model to detect alloy steel categories.•The data sets used in the experiment were all measured in real environments.•Compared with the current classification model of alloy steel, the model proposed in this paper can greatly improve the accuracy.