Interpretable and explainable rule-based modeling: analysis, design, and evaluation in the framework of Granular Computing and federated learning

可解释和可解释的基于规则的建模：粒度计算和联邦学习框架中的分析、设计和评估

• 批准号: RGPIN-2022-03045
• 负责人: Pedrycz, Witold
• 金额: $ 3.35万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752227
• 关键词: Interpretable; explainable; rule; based; modeling

Introduction With the heightened complexity of real-world systems and their advanced models, distributed sources of data, and increased expectations of the users, the quest for interpretable and explainable models. It becomes of paramount importance exhibiting tangible benefits, especially in system modeling and decision making in critical environments. Explainable system modeling is about developing models aimed at producing transparent knowledge about relationship existing in data/models and helping explain and audit prediction/classification results. The proposed Discovery Grant program is aimed at addressing the challenges of federated learning of explainable models with distributed data, analyzing innovative ways of evaluation of their performance and developing associated original methodologies and solutions. Objectives and originality We pursue a research program aimed at building a novel and efficient environment for designing and analyzing interpretable models such that they (i) possess sound and legitimate abstraction capabilities, (ii) exhibit the logic fabric of their structures, and (iii) are endowed with semantically sound mechanisms of evaluation of performance of the interpretable models and their results. We analyze how that these essential modeling features are efficiently realized with the aid of information granules (sets, fuzzy sets, rough sets) and how interpretable models can be constructed, studied and evaluated with the aid of Granular Computing. We design and evaluate quality of information granules regarded as building blocks fundamental to the formation of interpretable models. We develop new generalized techniques of data clustering essential to the formation of information granules. We construct and analyze innovative and efficient ways of federated learning of rule-based models. We establish efficient ways of assessing quality of interpretable models by engaging information granules of higher type. We open pioneering and far-reaching avenues of exploration of ways of assessing relevance of models through mechanisms of Granular Computing. Significance The research program will deliver substantive outcomes: Advancing fundamental knowledge via (i) breakthrough discoveries in Granular Computing and granular modeling. These discoveries open a new uncharted territory of the theory of advanced system modeling, concepts of explainability and a comprehensive evaluation environment, (ii) establishing new directions of granular explainable modeling, federated learning and evaluation of performance of models, (iii) exploring novel and transformative concepts of information granules, and (iv) analyzing and delivering quantifiable ways of evaluating granular results. Forming a range of original and transformative algorithms and design guidelines to tackle practical problems in system modeling, prediction, decision-making, and classification thus addressing the timely quest for advancements in complex system modeling.

随着现实世界系统及其高级模型的高度复杂性、分布式数据源以及用户对可解释和可解释模型的更高期望的增加而引入。它变得非常重要，显示出切实的好处，特别是在关键环境中的系统建模和决策制定方面。可解释的系统建模是关于开发模型，旨在产生关于数据/模型中存在的关系的透明知识，并帮助解释和审计预测/分类结果。拟议的发现资助计划旨在解决联合学习具有分布式数据的可解释模型的挑战，分析评估其性能的创新方法，并开发相关的原创方法和解决方案。目标和原创性我们追求的研究计划旨在建立一个新颖而有效的环境来设计和分析可解释模型，使其(I)具有合理和合法的抽象能力，(Ii)展示其结构的逻辑结构，以及(Iii)被赋予对可解释模型及其结果的性能进行语义评估的良好机制。我们分析了如何借助信息粒(集合、模糊集、粗糙集)有效地实现这些基本建模特征，以及如何借助粒计算来构造、研究和评估可解释模型。我们设计和评估信息颗粒的质量，将其视为形成可解释模型的基础。我们开发了新的通用数据聚类技术，这些技术对于信息颗粒的形成至关重要。构建并分析了创新高效的基于规则的联合学习方法。通过引入更高类型的信息颗粒，我们建立了评估可解释模型质量的有效方法。我们开辟了开拓性和深远的途径，探索通过粒计算机制评估模型相关性的方法。意义该研究计划将带来实质性成果：通过(I)粒计算和粒建模方面的突破性发现促进基础知识的发展。这些发现开辟了高级系统建模理论、可解释性概念和综合评估环境的新的未知领域，(Ii)建立了粒度可解释建模、联合学习和模型性能评估的新方向，(Iii)探索了新的和变革性的信息颗粒概念，以及(Iv)分析和提供了评估粒度结果的量化方法。形成一系列具有原创性和变革性的算法和设计指南，以解决系统建模、预测、决策和分类方面的实际问题，从而解决在复杂系统建模方面及时寻求进步的问题。