The method of selecting configurable hyperparameters of the intelligent classifier of unstructured text data according to the degree of confidentiality based on the hierarchy analysis method
Abstract
The method of selecting configurable hyperparameters of the intelligent classifier of unstructured text data according to the degree of confidentiality based on the hierarchy analysis method
Incoming article date: 05.03.2023A 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