集成数据挖掘知识的可解释最优超球体支持向量机OA北大核心CSTPCD

Small sphere and large margin support vector machine(SSLM)is a typical black box model,which works in no need of understanding the internal structure and mechanism of the object to be studied while only utilizes the input and output data for the purpose of knowing its function and interaction relation.Hence,the SSLM has the advantages of fast response and strong real-time performance,but accordingly lacks interpretability and transparency.In view of this,this paper examines ways to add prior knowledge into the input-port of the SSLM black box model to enhance its interpretability.We developed a nonlinear circular knowledge mining algorithm based on data as well as a discretization algorithm for knowledge,and the discrete data points contain not only the original data points that generated the knowledge,but also add new data points.By integrating the mined circular knowledge into the SSLM model in the form of inequality constraints,we construct an interpretable SSLM model(i-SSLM).When the model is trained,it is necessary to ensure that the data point classification of the knowledge constraint is correct,so there is a certain degree of prediction of the model results,indicating that the model is interpretable.At the same time,due to the discretization of knowledge to add new data information,the model can have higher accuracy.The validity of the i-SSLM model was verified on 10 sets of common sample sets and 2 sets of actual blast furnace datasets.