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

• Published in: Neural computing & applications 2024-11, Vol.36 (32), p.20493-20511
• Main Authors: Pereira, João Luiz Junho, Francisco, Matheus Brendon, Ma, Benedict Jun, Gomes, Guilherme Ferreira, Lorena, Ana Carolina
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Combinatorial analysis; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computing costs; Data Mining and Knowledge Discovery; Datasets; Feature selection; Fibonacci numbers; Heuristic methods; Image Processing and Computer Vision; K-nearest neighbors algorithm; Machine learning; Optimization; Original Article; Population distribution; Probability and Statistics in Computer Science; Sequences; Storage capacity; Transfer functions
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-10155-9

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.