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Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 4/2022

08.07.2021 | Research Paper

A comparative analysis of meta-heuristic optimization algorithms for feature selection and feature weighting in neural networks

verfasst von: P. M. Diaz, M. Julie Emerald Jiju

Erschienen in: Evolutionary Intelligence | Ausgabe 4/2022

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Abstract

Feature selection and feature weighting are frequently used in machine learning for processing high dimensional data. It reduces the number of features in the dataset and makes the classification process easier. Meta-heuristic algorithms are widely adopted for feature selection and feature weighting due to their enhanced searching ability. This paper compares five different meta-heuristic optimization algorithms that are recently introduced for feature selection and feature weighting in artificial neural networks. This includes chimp optimization algorithm, tunicate swarm algorithm, bear smell search algorithm, antlion optimization algorithm and modified antlion optimization algorithm. Experimental evaluations are performed on five different datasets to illustrate the significant improvements observed during classification process of all the algorithms utilised in the comparative analysis. Both tunicate swarm algorithm and chimp optimization algorithm has gained better classification accuracy than other algorithms. However, all these algorithms are found to be more effective for feature selection and feature weighting processes.
Vorheriger Artikel Multi-view fusion for recommendation with attentive deep neural network
Nächster Artikel Seeking a balance between population diversity and premature convergence for real-coded genetic algorithms with crossover operator

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Metadaten
Titel
A comparative analysis of meta-heuristic optimization algorithms for feature selection and feature weighting in neural networks
verfasst von
P. M. Diaz
M. Julie Emerald Jiju
Publikationsdatum
08.07.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Evolutionary Intelligence / Ausgabe 4/2022
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-021-00634-6

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