A new population initialization approach based on Metropolis–Hastings (MH) method

Cuevas, Erik; Escobar, Héctor; Sarkar, Ram; Eid, Heba F.

doi:10.1007/s10489-022-04359-6

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…This study adapted HGSO, a recent optimization technique that outperforms state-of-the-art optimization techniques; however, the original HGSO suffers from a random initialization issue, which often prolongs the convergence time and does not ensure that the highest global optimum is found. The existing problem with the traditional HGSO algorithm was addressed using the MH algorithm [23] to mitigate the population initialization problem of the traditional HGSO algorithm. The improved HGSO approach yields an enhanced convergence, requires fewer iterations, and achieves optimal values.…”

Section: Need For Hyperparameter Optimization and Proposed Hgsomentioning

confidence: 99%

“…Motivated by the need for hyperparameter optimization, this study adopts a novel HGSO algorithm [19], a recently introduced performance-based swarm optimization technique that demonstrates remarkable performance even when compared with an extensive collection of 23 reputable optimization functions and innovative algorithms, including the IEEE CEC 2014 benchmark test suite [20,21]. In addition, the Metropolis-Hastings (MH) algorithm was used instead of random initialization to adaptively initialize the hyperparameters [22,23]. Moreover, adaptive variational control (VC2) was required for robust optimization and to balance exploration and exploitation and thus converge to optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

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HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting

Pinki,

Awal,

Mumenin

et al. 2023

Mathematics

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Millions of people died in the COVID-19 pandemic, which pressured hospitals and healthcare workers into keeping up with the speed and intensity of the outbreak, resulting in a scarcity of ICU beds for COVID-19 patients. Therefore, researchers have developed machine learning (ML) algorithms to assist in identifying patients at increased risk of requiring an ICU bed. However, many of these studies used state-of-the-art ML algorithms with arbitrary or default hyperparameters to control the learning process. Hyperparameter optimization is essential in enhancing the classification effectiveness and ensuring the optimal use of ML algorithms. Therefore, this study utilized an improved Hunger Games Search Optimization (HGSO) algorithm coupled with a robust extreme gradient boosting (XGB) classifier to predict a COVID-19 patient’s need for ICU transfer. To further mitigate the random initialization inherent in HGSO and facilitate an efficient convergence toward optimal solutions, the Metropolis–Hastings (MH) method is proposed for integration with HGSO. In addition, population diversity was reintroduced to effectively escape local optima. To evaluate the efficacy of the MH-based HGSO algorithm, the proposed method was compared with the original HGSO algorithm using the Congress on Evolutionary Computation benchmark function. The analysis revealed that the proposed algorithm converges better than the original method and exhibits statistical significance. Consequently, the proposed algorithm optimizes the XGB hyperparameters to further predict the need for ICU transfer for COVID-19 patients. Various evaluation metrics, including the receiver operating curve (ROC), precision–recall curve, bootstrap ROC, and recall vs. decision boundary, were used to estimate the effectiveness of the proposed HGSOXGB model. The model achieves the highest accuracy of 97.39% and an area under the ROC curve of 99.10% compared with other classifiers. Additionally, the important features that significantly affect the prediction of ICU transfer need using XGB were calculated.

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Section: Need For Hyperparameter Optimization and Proposed Hgsomentioning

confidence: 99%