An improved Red-billed blue magpie feature selection algorithm for medical data processing DOI Creative Commons
Chenyi Zhu, Zhiyi Wang,

Yinan Peng

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(5), С. e0324866 - e0324866

Опубликована: Май 22, 2025

Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical analysis, large number complexity features are often accompanied by redundant or irrelevant features, which not only increase computational burden, but also may lead to model overfitting, turn affects its generalization ability. To address this problem, paper proposes an improved red-billed blue magpie algorithm (IRBMO), specifically optimized for feature task, significantly improves performance efficiency on introducing multiple innovative behavioral strategies. The core mechanisms IRBMO include: elite search behavior, global optimization guiding expand more promising directions; collaborative hunting quickly identifies key promotes among subsets; memory storage leverages historically valid information improve accuracy. adapt we convert continuous binary form via transfer function, further enhances applicability algorithm. order comprehensively verify IRBMO, designs series experiments compare it with nine mainstream algorithms. based 12 datasets, results show that achieves optimal overall metrics such as fitness value, classification accuracy specificity. addition, compared existing methods, demonstrates significant advantages terms value. enhance performance, constructs V2IRBMO variant combining S-shaped V-shaped functions, robustness ability Experiments demonstrate exhibits high efficiency, generality excellent tasks. used conjunction KNN classifier, accuracy, average improvement 43.89% datasets original Red-billed Blue Magpie These potential wide data.

Язык: Английский

Overview of the application of intelligent optimization algorithms in multi-attribute group decision making DOI

Kaiying Kang,

Jialiang Xie,

Xiaohui Liu

и другие.

Applied Intelligence, Год журнала: 2025, Номер 55(6)

Опубликована: Фев. 6, 2025

Язык: Английский

Процитировано

0
A comprehensive survey of golden jacal optimization and its applications DOI
Mehdi Hosseinzadeh, Jawad Tanveer, Amir Masoud Rahmani

и другие.

Computer Science Review, Год журнала: 2025, Номер 56, С. 100733 - 100733

Опубликована: Фев. 11, 2025

Язык: Английский

Процитировано

0
IBBA: an improved binary bat algorithm for solving low and high-dimensional feature selection problems DOI
Wang Tao,

Minzhu Xie

International Journal of Machine Learning and Cybernetics, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Язык: Английский

Процитировано

0
Hybrid strategy collaborative enhancement of white shark optimization algorithm DOI
Junchang Liu, Yu Liu,

Yahao Yang

и другие.

The Journal of Supercomputing, Год журнала: 2025, Номер 81(5)

Опубликована: Март 26, 2025

Язык: Английский

Процитировано

0
Newton Downhill Optimizer for Global Optimization DOI Creative Commons

Wanting Xiao,

Kaichen Ouyang, Junbo Lian

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Abstract The study presents the Newton's Downhill Optimizer (NDO), a novel metaheuristic algorithm designed to address challenges of complex, high-dimensional, and nonlinear optimization problems. Mathematical-Based Algorithms (MBAs) are category algorithms based on mathematical principles. They widely applied in numerical computation, symbolic manipulation, geometric processing, problems, probabilistic statistics, offering efficient precise solutions complex Inspired by Method, NDO combines its precision with downhill strategy stochastic processes, specifically real-world applications benchmark method inspired enhancing capability exploring solution space escaping local optima. In tests, demonstrated exceptional performance, surpassing majority competing multiple test suites CEC 2017 2022. We conducted comprehensive comparison against 14 well-established algorithms. These include mathematical-based approaches such as AOA, SCHO, SCA, SABO, NRBO, RUN. also compared it classical like CMA-ES, ABC, DE, PSO. Additionally, we included advanced recently published WSO, EHO, FDB_AGDEand GQPSO. results demonstrate that outperforms most these It exhibits superior convergence speed remarkable stability.In engineering applications, outperformed other reducer design task step-cone pulley delivered outstanding disk clutch brake tasks. A significant contribution is application breast cancer feature selection, tested two Breast datasets. performance accuracy, sensitivity, specificity, Matthews Correlation Coefficient (MCC), achieving accuracy across This underscores potential viable tool for addressing both medical fields. source codes will be shared at https://github.com/oykc1234/NDO.

Язык: Английский

Процитировано

0
A diversity enhanced tree-seed algorithm based on double search with genetic and automated learning search strategies for image segmentation DOI
Xianqiu Meng, Gaochao Xu, Xu Xu

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113143 - 113143

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
An improved Red-billed blue magpie feature selection algorithm for medical data processing DOI Creative Commons
Chenyi Zhu, Zhiyi Wang,

Yinan Peng

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(5), С. e0324866 - e0324866

Опубликована: Май 22, 2025

Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical analysis, large number complexity features are often accompanied by redundant or irrelevant features, which not only increase computational burden, but also may lead to model overfitting, turn affects its generalization ability. To address this problem, paper proposes an improved red-billed blue magpie algorithm (IRBMO), specifically optimized for feature task, significantly improves performance efficiency on introducing multiple innovative behavioral strategies. The core mechanisms IRBMO include: elite search behavior, global optimization guiding expand more promising directions; collaborative hunting quickly identifies key promotes among subsets; memory storage leverages historically valid information improve accuracy. adapt we convert continuous binary form via transfer function, further enhances applicability algorithm. order comprehensively verify IRBMO, designs series experiments compare it with nine mainstream algorithms. based 12 datasets, results show that achieves optimal overall metrics such as fitness value, classification accuracy specificity. addition, compared existing methods, demonstrates significant advantages terms value. enhance performance, constructs V2IRBMO variant combining S-shaped V-shaped functions, robustness ability Experiments demonstrate exhibits high efficiency, generality excellent tasks. used conjunction KNN classifier, accuracy, average improvement 43.89% datasets original Red-billed Blue Magpie These potential wide data.

Язык: Английский

Процитировано

0