Improvement of FSVM by the method of class overlap recognition based on SHAP value

In the classification problem, the phenomenon of class overlap will greatly affect the effectiveness of the classification model. A new method of class overlap recognition based on SHAP values is proposed for the identification of class overlap samples. Based on the SHAP value, the membership attribute of the sample's classification ability in the class to which it belongs is constructed to effectively identify the overlapping samples between classes. The applicability of the class overlap recognition method based on SHAP is verified by using simulation experiments; After normalizing the classification capability of samples, the membership measure of samples is constructed and applied to the fuzzy support vector machine (FSVM) algorithm to obtain FSVM_SHAP, The model has been tested on several classic binary data sets and achieved good results, which reflects the effectiveness of the model.