METHOD FOR INTERPRETING ARTIFICIAL NEURAL NETWORKS

摘要

The present technical solution relates in general to the field of computer technology, and in particular to methods and systems for interpreting the working of artificial neural network models. Claimed is a method for interpreting artificial neural networks which involves obtaining at least one artificial neural network pretrained on a set of objects; forming at least one decision tree for each layer of the trained neural network, said decision tree being produced as input data for activating the corresponding layer obtained when an object from the available data set passes through the neural network; predicting by means of the decision trees the response given to said object by the trained artificial neural network; then obtaining for each object an ordered sequence of numbers of the leaves of the decision trees formed in the preceding step; and generating a set of rules predicting the sequence of numbers of the leaves pertaining to an object. The technical result is an improvement in the quality and accuracy of the interpretation of the working of an artificial neural network.