Improved versions of snake optimizer for feature selection in medical diagnosis: a real case COVID-19 DOI
Malik Braik, Abdelaziz I. Hammouri, Mohammed A. Awadallah

et al.

Soft Computing, Journal Year: 2023, Volume and Issue: 27(23), P. 17833 - 17865

Published: Aug. 16, 2023

Language: Английский

A Survey on Evolutionary Multiobjective Feature Selection in Classification: Approaches, Applications, and Challenges DOI
Ruwang Jiao, Bach Hoai Nguyen, Bing Xue

et al.

IEEE Transactions on Evolutionary Computation, Journal Year: 2023, Volume and Issue: 28(4), P. 1156 - 1176

Published: July 5, 2023

Maximizing the classification accuracy and minimizing number of selected features are two primary objectives in feature selection, which is inherently a multiobjective task. Multiobjective selection enables us to gain various insights from complex data addition dimensionality reduction improved accuracy, has attracted increasing attention researchers practitioners. Over past decades, significant advancements have been achieved both methodologies applications, but not well summarized discussed. To fill this gap, paper presents broad survey on existing research classification, focusing up-to-date approaches, current challenges, future directions. be specific, we categorize basis different criteria, provide detailed descriptions representative methods each category. Additionally, summarize list successful real-world applications domains, exemplify their practical value demonstrate abilities providing set trade-off subsets meet requirements decision makers. We also discuss key challenges shed lights emerging directions for developments selection.

Language: Английский

Citations

62
CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection DOI Creative Commons

Mohammed Abdelrazek,

Mohamed Abd Elaziz,

A. H. El-Baz

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 6, 2024

Abstract In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO novel technique the swarm intelligence algorithms which mimic foraging behavior Mongoose. The developed method, named Chaotic (CDMO), considered wrapper-based model selects optimal features that give higher classification accuracy. To speed up convergence and increase effectiveness DMO, ten chaotic maps were used to modify key elements movement during optimization process. evaluate efficiency CDMO, different UCI datasets are compared against original other well-known Meta-heuristic techniques, namely Ant Colony (ACO), Whale algorithm (WOA), Artificial rabbit (ARO), Harris hawk (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale (RSGW), Salp on particle (SSAPSO), Binary genetic (BGA), Adaptive (ASGW) Particle Swarm (PSO). experimental results show CDMO gives performance than methods in selection. High value accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) F-Score (90–100%) all obtained. addition, proposed method further assessed CEC’2022 benchmarks functions.

Language: Английский

Citations

28
Evolutionary computation for feature selection in classification: A comprehensive survey of solutions, applications and challenges DOI

Xianfang Song,

Yong Zhang, Wanqiu Zhang

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 90, P. 101661 - 101661

Published: July 22, 2024

Language: Английский

Citations

19
Competitive Swarm Optimizer: A decade survey DOI
Dikshit Chauhan,

Shivani,

Ran Cheng

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 87, P. 101543 - 101543

Published: April 4, 2024

Language: Английский

Citations

17
Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering DOI Creative Commons
Chrysanthos Maraveas, Panagiotis G. Asteris, Konstantinos G. Arvanitis

et al.

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 30(3), P. 1979 - 2012

Published: Dec. 20, 2022

Abstract The article reviewed the four major Bioinspired intelligent algorithms for agricultural applications, namely ecological, swarm-intelligence-based, ecology-based, and multi-objective algorithms. key emphasis was placed on variants of swarm intelligence algorithms, artificial bee colony (ABC), genetic algorithm, flower pollination algorithm (FPA), particle swarm, ant colony, firefly fish Krill herd because they had been widely employed in sector. There a broad consensus among scholars that certain BIAs' were more effective than others. For example, Ant Colony Optimization Algorithm best suited farm machinery path optimization pest detection, other applications. On contrary, useful determining plant evapotranspiration rates, which predicted water requirements irrigation process. Despite promising adoption hyper-heuristic agriculture remained low. No universal could perform multiple functions farms; different designed to specific functions. Secondary concerns relate data integrity cyber security, considering history cyber-attacks smart farms. concerns, benefits associated with BIAs outweighed risks. average, farmers can save 647–1866 L fuel is equivalent US$734-851, use GPS-guided systems. accuracy mitigated risk errors applying pesticides, fertilizers, irrigation, crop monitoring better yields.

Language: Английский

Citations

43
A tutorial-based survey on feature selection: Recent advancements on feature selection DOI
Amir Moslemi

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 126, P. 107136 - 107136

Published: Sept. 21, 2023

Language: Английский

Citations

33
An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets DOI
Reham R. Mostafa,

Ahmed M. Khedr,

Zaher Al Aghbari

et al.

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 283, P. 111218 - 111218

Published: Nov. 21, 2023

Language: Английский

Citations

30
Centric graph regularized log-norm sparse non-negative matrix factorization for multi-view clustering DOI

Yuzhu Dong,

Hangjun Che, Man-Fai Leung

et al.

Signal Processing, Journal Year: 2023, Volume and Issue: 217, P. 109341 - 109341

Published: Nov. 23, 2023

Language: Английский

Citations

27
WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection DOI Creative Commons
Muhammad Umair Ali, Shaik Javeed Hussain, Amad Zafar

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(4), P. 475 - 475

Published: April 14, 2023

This study presents wrapper-based metaheuristic deep learning networks (WBM-DLNets) feature optimization algorithms for brain tumor diagnosis using magnetic resonance imaging. Herein, 16 pretrained are used to compute the features. Eight algorithms, namely, marine predator algorithm, atom search algorithm (ASOA), Harris hawks butterfly whale grey wolf (GWOA), bat and firefly evaluate classification performance a support vector machine (SVM)-based cost function. A deep-learning network selection approach is applied determine best network. Finally, all features of concatenated train SVM model. The proposed WBM-DLNets validated based on an available online dataset. results reveal that accuracy significantly improved by utilizing selected relative those obtained full set DenseNet-201-GWOA EfficientNet-b0-ASOA yield results, with 95.7%. Additionally, compared reported in literature.

Language: Английский

Citations

23
Semi-supervised feature selection by minimum neighborhood redundancy and maximum neighborhood relevancy DOI

Damo Qian,

Keyu Liu, Shiming Zhang

et al.

Applied Intelligence, Journal Year: 2024, Volume and Issue: 54(17-18), P. 7750 - 7764

Published: June 13, 2024

Language: Английский

Citations

15