Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization

Hosny, Rodyna A.; Elaziz, Mohamed Abd; Ibrahim, Rehab Ali

doi:10.1155/2022/3991870

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…Lashin et al [5] used topological notions to study different issues in rough set theory in order to generalize Pawlak's concepts [6] in different applications and integrate the concepts of rough and fuzzy sets. Topological structures were applied in rough sets to improve evolutionary-based feature selection technique using the extension of knowledge [7], decision making of COVID-19 [8,9], and enhanced feature selection based on integration containment neighborhoods rough set approximations and binary honey badger optimization [10]. Several articles [11][12][13][14][15][16][17][18][19] extended the application fields of Pawlak's model.…”

Section: Introductionmentioning

confidence: 99%

Approximations by Ideal Minimal Structure with Chemical Application

Hosny¹,

Abu-Gdairi²,

El-Bably³

2023

Intelligent Automation &Amp; Soft Computing

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The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information. It can be characterized by two crisp sets, named the upper and lower approximations that are used to determine the boundary region and accurate measure of any subset. This article endeavors to achieve the best approximation and the highest accuracy degree by using the minimal structure approximation space MSAS via ideal J . The novel approach (indicated by J MSAS) modifies the approximation space to diminish the boundary region and enhance the measure of accuracy. The suggested method is more accurate than Pawlak's and EL-Sharkasy techniques. Via illustrated examples, several remarkable results using these notions are obtained and some of their properties are established. Several sorts of near open (resp. closed) sets based on J MSAS are studied. Furthermore, the connections between these assorted kinds of near-open sets in J MSAS are deduced. The advantages and disadvantages of the proposed approach compared to previous ones are examined. An algorithm using MATLAB and a framework for decision-making problems are verified. Finally, the chemical application for the classification of amino acids (AAs) is treated to highlight the significance of applying the suggested approximation.

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Section: Introductionmentioning

confidence: 99%

Approximations by Ideal Minimal Structure with Chemical Application

Hosny¹,

Abu-Gdairi²,

El-Bably³

2023

Intelligent Automation &Amp; Soft Computing

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“…However, the HBA suffers from the same flaws as other metaheuristic algorithms, such as a lack of global exploration capability, sluggish convergence speed, low accuracy, and the ease with which it can fall into a local optimum. Scholars have improved the HBA, and the improved algorithm was used to proton exchange membrane fuel cells [28], feature selection [29], extreme learning machines [30], and other research areas. However, only a few researchers have used the improved HBA to identify solar photovoltaic cell parameters.…”

Section: Introductionmentioning

confidence: 99%

Solar Photovoltaic Cell Parameter Identification Based on Improved Honey Badger Algorithm

Lei

Yang

et al. 2022

Sustainability

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Photovoltaic technology, which converts the sun’s light energy directly into electricity, can be used to make photovoltaic cells. The use of photovoltaic cells is centered on the idea of a low-carbon economy and green environmental protection, which effectively addresses the pollution problem in smart cities. Accurate identification of photovoltaic cell parameters is critical for battery life cycle and energy utilization. To accurately identify the single diode model (SDM), dual diode model (DDM), and three diode model (TDM) parameters of solar photovoltaic cells, and an improved honey badger algorithm (IHBA) is proposed in this paper. In the early stages of iteration, the IHBA uses the spiral exploration mechanism to improve the population’s global exploration ability. Furthermore, a density update factor that varies according to the quasi-cosine law is introduced to speed up the algorithm’s convergence speed and prevent the algorithm from falling into the local optimal value. Simultaneously, the pinhole imaging strategy is utilized to disturb the present optimal position to improve the algorithm’s optimization accuracy. The experimental comparison results of 18 benchmark test functions, Wilcoxon rank sum statistical test, and 30 CEC2014 test functions reveal that an IHBA shows remarkable performance in convergence speed, optimization accuracy, and robustness. Finally, the IHBA is used to identify the parameters of three kinds of commercial silicon R.T.C French solar photovoltaic cell models with a 57 mm diameter. In comparison to other algorithms, the IHBA can minimize the root mean square error (RMSE) between the measured current and estimated current at the fastest speed, demonstrating the practicality and superiority of the IHBA in tackling this problem.

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Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

Kinikar,

Saleena

2024

Multimed Tools Appl

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Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization

Cited by 11 publications

References 51 publications

Approximations by Ideal Minimal Structure with Chemical Application

Approximations by Ideal Minimal Structure with Chemical Application

Solar Photovoltaic Cell Parameter Identification Based on Improved Honey Badger Algorithm

Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

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