Variable-length differential evolution for numerical and discrete association rule mining

Uroš Mlakar (Avtor), Iztok Fister (Avtor), Iztok Fister (Avtor)

Povzetek

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.

Ključne besede

diferencialna evolucija;podatkovno rudarjenje;predstavitev rešitev;association rule mining;differential evolution;data mining;variable-lenght solution representation;green AI;

Podatki

Jezik:	Angleški jezik
Leto izida:	2023
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UM FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
Založnik:	Institute of Electrical and Electronics Engineers (IEEE)
UDK:	004.8
COBISS:	181528835
ISSN:	2169-3536
Št. ogledov:	265
Št. prenosov:	2
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	diferencialna evolucija;podatkovno rudarjenje;predstavitev rešitev;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 4239-4254
Letnik:	ǂVol. ǂ12
Čas izdaje:	29 Dec. 2023
DOI:	10.1109/ACCESS.2023.3348408
ID:	22427607