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Variable-Length Differential Evolution for Numerical and Discrete Association Rule Mining
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DOI：10.1109/ACCESS.2023.3348408.png)
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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.
Keyword:
Data mining
Genetic algorithms
Artificial intelligence
Statistics
Sociology
Measurement
Indexes
Association rule mining
differential evolution
data mining
variable-length solution representation
green AI
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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