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  • Variable-length differential evolution for numerical and discrete association rule mining [Elektronski vir]
    Mlakar, Uroš, 1988- ; Fister, Iztok, ml. ; Fister, Iztok
    This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and ... discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.
    Vir: IEEE access [Elektronski vir]. - ISSN 2169-3536 (Vol. 12, 29 Dec. 2023, str. 4239-4254)
    Vrsta gradiva - e-članek ; neleposlovje za odrasle
    Leto - 2023
    Jezik - angleški
    COBISS.SI-ID - 181528835

    Povezava(-e):

    Digitalna knjižnica Univerze v Mariboru – DKUM
    DOI

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