Golden lichtenberg algorithm: a fibonacci sequence approach applied to feature selection
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521_2024_10155_ReferencePDF.pdf
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Author(s)
Pereira, João L. J.
Francisco, Matheus B.
Ma, Benedict J.
Gomes, Guilherme F.
Lorena, Ana C.
Date Issued
August 13, 2024
Journal
Neural Computing and Applications
Publisher
Springer London
Citation
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 36, 20493–20511 (2024).
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s00521-024-10155-9
