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

Title:IEEE access
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2169-3536
COBISS.SI-ID:519839513 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P2-0041
Name:Računalniški sistemi, metodologije in inteligentne storitve

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:diferencialna evolucija, podatkovno rudarjenje, predstavitev rešitev


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