A structural model of an intelligent classifier of unstructured textual data according to the degree of confidentiality is presented, which is a two-level cascading ensemble of classifier models. The meta-model of a fully connected neural network architecture, which has the greatest impact on the classification efficiency, is highlighted. The multi-criteria task of configuring the intelligent classifier is decomposed into the task of selecting configurable hyperparameters of the meta-model and the task of selecting their values. Taking into account the selected hyperparameters of the neural network meta-model, the multi-criteria task of selecting hyperparameters to be configured is presented in the form of a hierarchy that includes the goal, criteria and alternatives. A method for selecting configurable hyperparameters of an intelligent classifier of unstructured text data by the degree of confidentiality based on the hierarchy analysis method has been developed.

Keywords: DLP system, unstructured text data, intelligent classifier, hyperparameters, hierarchy analysis method