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Computer Science and Education in Computer Science. 20th EAI International Conference, CSECS 2024, Sofia, Bulgaria, June 28–30, 2024, Proceedings

Research Article

MaRz: A Fast, Transparent Fuzzy Machine Learning Technique

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BibTeX Plain Text
  • @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
Eric Braude1,*, Seth Gorrin1
  • 1: Boston University, Boston
*Contact email: ebraude@bu.edu

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.

Keywords
Explainable machine learning Real-time machine learning Fuzzy machine learning
Published
2025-03-14
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-84312-9_4
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