Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer DOI Creative Commons
Mansourah Aljohani, Yousry AbdulAzeem, Hossam Magdy Balaha

и другие.

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(3), С. 308 - 325

Опубликована: Май 1, 2024

Abstract Feature selection (FS) is vital in improving the performance of machine learning (ML) algorithms. Despite its importance, identifying most important features remains challenging, highlighting need for advanced optimization techniques. In this study, we propose a novel hybrid feature ranking technique called Hybrid Ranking Weighted Majority Model (HFRWM2). HFRWM2 combines ML models with Harris Hawks Optimizer (HHO) metaheuristic. HHO known versatility addressing various challenges, thanks to ability handle continuous, discrete, and combinatorial problems. It achieves balance between exploration exploitation by mimicking cooperative hunting behavior Harris’s hawks, thus thoroughly exploring search space converging toward optimal solutions. Our approach operates two phases. First, an odd number models, conjunction HHO, generate encodings along metrics. These are then weighted based on their metrics vertically aggregated. This process produces rankings, facilitating extraction top-K features. The motivation behind our research 2-fold: enhance precision algorithms through optimized FS improve overall efficiency predictive models. To evaluate effectiveness HFRWM2, conducted rigorous tests datasets: “Australian” “Fertility.” findings demonstrate navigating We compared 12 other techniques found it outperform them. superiority was particularly evident graphical comparison dataset, where showed significant advancements ranking.

Язык: Английский

Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives DOI Creative Commons
Amir Aghsami, Simintaj Sharififar, Nader Markazi Moghaddam

и другие.

Systems, Год журнала: 2024, Номер 12(6), С. 215 - 215

Опубликована: Июнь 18, 2024

Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, vehicles, medical facilities, which can mitigate human casualties address needs across affected areas. However, despite the importance of managing disasters within organizational networks, there remains research gap in development mathematical models for scenarios, specifically incorporating offices external stakeholders relief centers. Addressing this gap, study examines an optimization model both before after planning humanitarian supply chain logistical framework organization. The areas are defined stakeholders, facilities. A mixed-integer nonlinear is formulated minimize overall costs, considering factors penalty costs untreated injuries demand, delays rescue item distribution operations, waiting injured emergency vehicles air ambulances. implemented using GAMS software 47.1.0 test problems different scales, with Grasshopper Optimization Algorithm proposed larger-scale scenarios. Numerical examples provided show effectiveness feasibility validate metaheuristic approach. Sensitivity analysis conducted assess model’s performance under conditions, key managerial insights implications discussed.

Язык: Английский

Процитировано

4
ESARSA-MRFO-FS: Optimizing Manta-ray Foraging Optimizer using Expected-SARSA reinforcement learning for features selection DOI
Yousry AbdulAzeem, Hossam Magdy Balaha, Amna Bamaqa

и другие.

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113695 - 113695

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer DOI Creative Commons
Mansourah Aljohani, Yousry AbdulAzeem, Hossam Magdy Balaha

и другие.

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(3), С. 308 - 325

Опубликована: Май 1, 2024

Abstract Feature selection (FS) is vital in improving the performance of machine learning (ML) algorithms. Despite its importance, identifying most important features remains challenging, highlighting need for advanced optimization techniques. In this study, we propose a novel hybrid feature ranking technique called Hybrid Ranking Weighted Majority Model (HFRWM2). HFRWM2 combines ML models with Harris Hawks Optimizer (HHO) metaheuristic. HHO known versatility addressing various challenges, thanks to ability handle continuous, discrete, and combinatorial problems. It achieves balance between exploration exploitation by mimicking cooperative hunting behavior Harris’s hawks, thus thoroughly exploring search space converging toward optimal solutions. Our approach operates two phases. First, an odd number models, conjunction HHO, generate encodings along metrics. These are then weighted based on their metrics vertically aggregated. This process produces rankings, facilitating extraction top-K features. The motivation behind our research 2-fold: enhance precision algorithms through optimized FS improve overall efficiency predictive models. To evaluate effectiveness HFRWM2, conducted rigorous tests datasets: “Australian” “Fertility.” findings demonstrate navigating We compared 12 other techniques found it outperform them. superiority was particularly evident graphical comparison dataset, where showed significant advancements ranking.

Язык: Английский

Процитировано

2