title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ¸ÑÑÐµÐ¼Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° ÐºÐ¾Ð½ÑÐµÐ¿ÑÐ°Ñ Ð´Ð»Ñ Ð¸Ð½ÑÐµÐ³ÑÐ°ÑÐ¸Ð¸ ÑÐºÑÐ¿ÐµÑÑÐ½ÑÑ Ð¿ÑÐ°Ð²Ð¸Ð» Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development and research of an interpretable concept learning model for integrating expert rules into machine learning models
Creators: name: ÐÑÑÐ¾Ð²Ð°, Ð¡Ð¾ÑÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ¸ÑÑÐµÐ¼ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½Ð° ÐºÐ¾Ð½ÑÐµÐ¿ÑÐ°Ñ
ÑÐºÑÐ¿ÐµÑÑÐ½ÑÐµ Ð¿ÑÐ°Ð²Ð¸Ð»Ð°
Ð¸Ð½Ð´ÑÐºÑÐ¸Ð²Ð½Ð¾Ðµ Ð¸ Ð´ÐµÐ´ÑÐºÑÐ¸Ð²Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
interpretable learning models
concept learning
expert rules
inductive and deductive learning
neural networks

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description: The goal of this thesis is to develop and research an interpretable concept learning model that combines deductive and inductive learning through the integration of expert rules into machine learning models to enhance prediction accuracy and improve the explainability of the models decisions. Subject of Research: The development of a concept learning model that integrates expert rules and the analysis of its impact on the accuracy and interpretability of machine learning models. Research Tasks: 1. Conduct a review of existing machine learning methods and models with an emphasis on interpretability. 2. Develop an algorithm for integrating expert rules into concept-based machine learning models. 3. Implement the proposed algorithm in Python using appropriate frameworks and libraries. 4. Conduct experiments to evaluate the effectiveness and interpretability of the developed model on real-world data. 5. Analyze experimental results and evaluate their significance. As a result of this work, an algorithm for concept learning was implemented, capable of incorporating expert rules and data while ensuring the interpretability of decisions. The performance of this tool was demonstrated on two generated datasets, yielding results that confirm its effectiveness and applicability. The obtained results can serve as a basis for further research and application development in various fields requiring high interpretability of machine learning models, such as medicine, finance, and law.