[2604.00256] Informed Machine Learning with Knowledge Landmarks

Computer Science > Machine Learning arXiv:2604.00256 (cs) [Submitted on 31 Mar 2026] Title:Informed Machine Learning with Knowledge Landmarks Authors:Chuyi Dai, Witold Pedrycz, Suping Xu, Ding Liu, Xianmin Wang View a PDF of the paper titled Informed Machine Learning with Knowledge Landmarks, by Chuyi Dai and 4 other authors View PDF HTML (experimental) Abstract:Informed Machine Learning has emerged as a viable generalization of Machine Learning (ML) by building a unified conceptual and algorithmic setting for constructing models on a unified basis of knowledge and data. Physics-informed ML involving physics equations is one of the developments within Informed Machine Learning. This study proposes a novel direction of Knowledge-Data ML, referred to as KD-ML, where numeric data are integrated with knowledge tidbits expressed in the form of granular knowledge landmarks. We advocate that data and knowledge are complementary in several fundamental ways: data are precise (numeric) and local, usually confined to some region of the input space, while knowledge is global and formulated at a higher level of abstraction. The knowledge can be represented as information granules and organized as a collection of input-output information granules called knowledge landmarks. In virtue of this evident complementarity, we develop a comprehensive design process of the KD-ML model and formulate an original augmented loss function L, which additively embraces the component responsible for opt...