Title: | Variable-length differential evolution for numerical and discrete association rule mining |
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Authors: | ID Mlakar, Uroš (Author) ID Fister, Iztok (Author) ID Fister, Iztok (Author) |
Files: | Variable-Length_Differential_Evolution_for_Numerical_and_Discrete_Association_Rule_Mining.pdf (2,39 MB) MD5: 2B61B6274D0263F7D2ABA60AE26D1013
https://ieeexplore.ieee.org/document/10376180
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Language: | English |
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Work type: | Article |
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Typology: | 1.01 - Original Scientific Article |
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Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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Abstract: | 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. |
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Keywords: | association rule mining, differential evolution, data mining, variable-lenght solution representation, green AI |
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Publication status: | Published |
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Publication version: | Version of Record |
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Submitted for review: | 24.11.2023 |
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Article acceptance date: | 27.12.2023 |
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Publication date: | 29.12.2023 |
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Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
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Year of publishing: | 2023 |
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Number of pages: | str. 4239-4254 |
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Numbering: | Vol. 12 |
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PID: | 20.500.12556/DKUM-86784  |
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UDC: | 004.8 |
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ISSN on article: | 2169-3536 |
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COBISS.SI-ID: | 181528835  |
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DOI: | 10.1109/ACCESS.2023.3348408  |
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Copyright: | 2023 The Authors |
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Publication date in DKUM: | 18.01.2024 |
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Views: | 341 |
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Downloads: | 25 |
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Metadata: |  |
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Categories: | Misc.
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