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Golden lichtenberg algorithm: a fibonacci sequence approach applied to feature selection

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Abstract

Computational and technological advancements have led to an increase in data generation and storage capacity. Many annotated datasets have been used to train machine learning models for predictive tasks. Feature selection (FS) is a combinatorial binary optimization problem that arises from a need to reduce dataset dimensionality by finding the subset of features with maximum predictive accuracy. While different methodologies have been proposed, metaheuristics adapted to binary optimization have proven to be reliable and efficient techniques for FS. This paper applies the first and unique population-trajectory metaheuristic, the Lichtenberg algorithm (LA), and enhances it with a Fibonacci sequence to improve its exploration capabilities in FS. Substituting the random scales that controls the Lichtenberg figures' size and the population distribution in the original version by a sequence based on the golden ratio, a new optimal exploration–exploitation LF's size decay is presented. The new few hyperparameters golden Lichtenberg algorithm (GLA), LA, and eight other popular metaheuristics are then equipped with the v-shaped transfer function and associated with the K-nearest neighbor classifier in the search of the optimized feature subsets through a double cross-validation experiment method on 15 UCI machine learning repository datasets. The binary GLA selected reduced subsets of features, leading to the best predictive accuracy and fitness values at the lowest computational cost.

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Data availability

The datasets used are available in the UCI machine learning repository, the link is referred as https://archive.ics.uci.edu/ml/index.php. The MATLAB code of the Lichtenberg algorithm can be found at https://www.mathworks.com/matlabcentral/fileexchange/84732-lichtenberg-algorithm-la.

Abbreviations

DM:

Data mining

ML:

Machine learning

FS:

Feature selection

NFL:

No-free-lunch theorem

LA:

Lichtenberg algorithm

GLA:

Golden Lichtenberg algorithm

DLA:

Diffusion-limited aggregation

LF:

Lichtenberg figure

S :

Stickiness factor

N p :

Particle's number

R c :

Creation radius

Ref :

Refinement

M :

Figure switching factor

Pop :

Population

N iter :

Iteration's Number

KNN:

K-nearest neighborhood

BLA:

Binary Lichtenberg algorithm

MH:

Metaheuristics

PSO:

Particle swarm optimization

DE:

Differential evolution

GA:

Genetic algorithm

MBO:

Monarch butterfly optimization

SSA:

Salp swarm algorithm

WOA:

Whale optimization algorithm

HHO:

Harris Hawks optimization

MRFO:

Manta ray foraging optimization

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Acknowledgements

The authors acknowledge the financial support from the FAPESP (São Paulo Research Foundation, grants #2023/10419-0, #2022/10683-7, and #2021/06870-3) and FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais, grant APQ-00385-18).

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Authors and Affiliations

  1. Computer Science Division, Aeronautics Institute of Technology (ITA), São José dos Campos, Brazil

    João Luiz Junho Pereira

  2. Institute of Industrial Engineering and Manegement, Federal University of Itajubá (UNIFEI), Itajubá, Brazil

    Matheus Brendon Francisco

  3. Center for Transportation & Logistics, Massachusetts Institute of Technology (MIT), Cambridge, USA

    Benedict Jun Ma

  4. Mechanical Engineering Institute, Federal University of Itajubá (UNIFEI), Itajubá, Brazil

    Guilherme Ferreira Gomes

  5. Computer Science Division, Aeronautics Institute of Technology (ITA), São José Dos Campos, Brazil

    Ana Carolina Lorena

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Pereira, J.L.J., Francisco, M.B., Ma, B.J. et al. Golden lichtenberg algorithm: a fibonacci sequence approach applied to feature selection. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-10155-9

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