Интерпретация моделей машинного обучения с помощью методов, основанных на соответствиях ГалуаInterpreting machine learning models with methods based on Galois connections

This dissertation presents a principled framework for explainable machine learning based on Galois connections, specifically leveraging Formal Concept Analysis (FCA) and pattern structures. Focusing on document classification, it proposes inherently explainable models that integrate symbolic reasoning with semantic document representations. There are three core contributions: (1) an FCA-based model that constructs class-specific concept lattices to classify documents and generate interpretable keyword cluster-based explanations, (2) a pattern structure-based aggregate rule classifier over Abstract Meaning Representation (AMR)-based document graphs that identifies stable, semantically meaningful subgraphs and related text fragments as explanations, and (3) a hybrid symbolic-neural model that combines Concept Whitening (CW) and Graph Neural Networks (GNNs) to align the latent dimensions of the GNNs with interpretable graph concepts. The models are evaluated using various metrics for classification accuracy and explanation quality. The results show that Galois connection-based methods can create accurate, inherently interpretable classifiers and provide a formal foundation for explainability in structured data.