A Survey on Evolutionary Multiobjective Feature Selection in Classification: Approaches, Applications, and Challenges

IEEE Transactions on Evolutionary Computation, Год журнала: 2023, Номер 28(4), С. 1156 - 1176

Опубликована: Июль 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.

Язык: Английский

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CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 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.

Язык: Английский

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

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 90, С. 101661 - 101661

Опубликована: Июль 22, 2024

Язык: Английский

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Competitive Swarm Optimizer: A decade survey

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 87, С. 101543 - 101543

Опубликована: Апрель 4, 2024

Язык: Английский

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Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Archives of Computational Methods in Engineering, Год журнала: 2022, Номер 30(3), С. 1979 - 2012

Опубликована: Дек. 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.

Язык: Английский

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An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets

Knowledge-Based Systems, Год журнала: 2023, Номер 283, С. 111218 - 111218

Опубликована: Ноя. 21, 2023

Язык: Английский

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A tutorial-based survey on feature selection: Recent advancements on feature selection

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 126, С. 107136 - 107136

Опубликована: Сен. 21, 2023

Язык: Английский

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

Signal Processing, Год журнала: 2023, Номер 217, С. 109341 - 109341

Опубликована: Ноя. 23, 2023

Язык: Английский

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Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion

Applied Energy, Год журнала: 2023, Номер 353, С. 122135 - 122135

Опубликована: Окт. 24, 2023

Язык: Английский

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

Applied Intelligence, Год журнала: 2024, Номер 54(17-18), С. 7750 - 7764

Опубликована: Июнь 13, 2024

Язык: Английский

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