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  • Silas: A high-performance machine learning foundation for logical reasoning and verification

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    Embargoed until: 2023-03-08
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    Version of Record (VoR)
    Author(s)
    Bride, H
    Cai, CH
    Dong, J
    Dong, JS
    Hóu, Z
    Mirjalili, S
    Sun, J
    Griffith University Author(s)
    Dong, Jin-Song
    Mirjalili, Seyedali
    Hou, Zhe
    Year published
    2021
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    Abstract
    This paper introduces a new high-performance machine learning tool named Silas, which is built to provide a more transparent, dependable and efficient data analytics service. We discuss the machine learning aspects of Silas and demonstrate the advantage of Silas in its predictive and computational performance. We show that several customised algorithms in Silas yield better predictions in a significantly shorter time compared to the state-of-the-art. Another focus of Silas is on providing a formal foundation of decision trees to support logical analysis and verification of learned prediction models. We illustrate the potential ...
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    This paper introduces a new high-performance machine learning tool named Silas, which is built to provide a more transparent, dependable and efficient data analytics service. We discuss the machine learning aspects of Silas and demonstrate the advantage of Silas in its predictive and computational performance. We show that several customised algorithms in Silas yield better predictions in a significantly shorter time compared to the state-of-the-art. Another focus of Silas is on providing a formal foundation of decision trees to support logical analysis and verification of learned prediction models. We illustrate the potential capabilities of the fusion of machine learning and logical reasoning by showcasing applications in three directions: formal verification of the prediction model against user specifications, training correct-by-construction models, and explaining the decision-making of predictions.
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    Journal Title
    Expert Systems with Applications
    Volume
    176
    DOI
    https://doi.org/10.1016/j.eswa.2021.114806
    Copyright Statement
    © 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Machine learning
    Publication URI
    http://hdl.handle.net/10072/403779
    Collection
    • Journal articles

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