
Research Article
MaRz: A Fast, Transparent Fuzzy Machine Learning Technique
@INPROCEEDINGS{10.1007/978-3-031-84312-9_4, author={Eric Braude and Seth Gorrin}, title={MaRz: A Fast, Transparent Fuzzy Machine Learning Technique}, proceedings={Computer Science and Education in Computer Science. 20th EAI International Conference, CSECS 2024, Sofia, Bulgaria, June 28--30, 2024, Proceedings}, proceedings_a={CSECS}, year={2025}, month={3}, keywords={Explainable machine learning Real-time machine learning Fuzzy machine learning}, doi={10.1007/978-3-031-84312-9_4} }
- Eric Braude
Seth Gorrin
Year: 2025
MaRz: A Fast, Transparent Fuzzy Machine Learning Technique
CSECS
Springer
DOI: 10.1007/978-3-031-84312-9_4
Abstract
There is significant interest in fast machine learning and in explainability. This paper’s contribution is a novel, but straightforward, fuzzy model that learns on the fly, is accurate, and explains its conclusions in a literal manner. MaRz (Machine Learning in Realtime with Fuzziness) treats each record as fuzzy and applies classical fuzzy center-of-gravity calculations. In the interest of trustworthiness, MaRz does not attempt a generalized form of explanation. Instead, it shows the specific data that most contributed to the output and allows those data to be tested in the context of the remaining data. It places at the user’s discretion how many such data to provide and thereby increase the explanation. The contribution of this paper is to demonstrate a machine learning approach for categorization and regression of competitive accuracy that is, at the same time, novel, real time, and explainable.