A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection

Fang, Lingling; Liang, Xiyue

doi:10.1007/s42235-022-00253-6

Cited by 23 publications

(5 citation statements)

References 40 publications

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“…Abayomi-Alli et al [ 59 ] demonstrated a Bidirectional Long Short-Term Memory (BiLSTM) for the UCI PD dataset, and their model achieved an accuracy of 82.86% with the original data. Fang and Liang [ 60 ] presented the UCI dataset for Parkinson’s disease and optimization algorithms such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Binary PSO (BPSO), and Binary GOA (BGOA) compared to the Nonlinear Binary Grasshopper Whale Optimization Algorithm (NL-BGWOA), and the results showed that the NL-BGWOA achieved 91.30% higher than other optimization algorithms. Figure 10 demonstrates the comparative study of the proposed method based on BO-SVM with the mentioned methods based on the same applied PD standard dataset.…”

Section: Resultsmentioning

confidence: 99%

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Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Elshewey

Shams

El-Rashidy

et al. 2023

Sensors

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Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.

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Section: Resultsmentioning

confidence: 99%

“… Comparison between the proposed model and the recent approaches [ 56 , 57 , 58 , 59 , 60 ] using the same standard PD dataset. …”

Section: Figurementioning

confidence: 99%

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Elshewey

Shams

El-Rashidy

et al. 2023

Sensors

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“…These applications can be broken down into three categories: screening, prediction, and diagnosis [7,16]. Combining MAs with FS methods from clinical datasets has resulted in the recent development of diagnostic methods for detecting individuals infected with the COVID-19 [37,100,101]. This paper uses and evaluates the BMCMBO algorithm to predict patients with the COVID-19 using an FS model combined with the KNN classifier.…”

Section: Application Of Bmcmbo Algorithm In Fs For the Covid-19 Diagn...mentioning

confidence: 99%

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Parizi

2024

Preprint

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Recent technological advances in medical diagnosis have led to the generation of high-dimensional datasets. The presence of redundant and irrelevant features in these datasets can have adverse effects on the performance of machine learning (ML) methods and reduce the accuracy of their results. Therefore, feature selection (FS), i.e., a popular preprocessing method in ML, is used to select the optimal subsets of features to improve the accuracy of ML methods. This performance enhancement is more crucial while addressing high-dimensional medical issues. Since FS is a multiobjective binary optimization problem, it is necessary to develop efficient FS algorithms. Although metaheuristic algorithms (MAs) have been widely used for FS in medicine, they face different challenges in most applications, e.g., a lack of sufficient effectiveness and scalability to select the most effective features in small and large medical datasets. The cat and mouse-based optimizer (CMBO) is a novel MA based on the natural competitive behavior of cats and mice. Despite its acceptable performance in a variety of problems, the CMBO faces various challenges such as limited exploitation abilities, an unbalanced search mechanism, and high fluctuation in solutions to complex problems, e.g., FS. This paper proposes a modified and binary version of the CMBO called the BMCMBO to enhance the performance in selecting effective features from medical datasets. The BMCMBO involves significant modifications to the method of updating the positions of search agents, the method of selecting mice, the effect of the positional information of the most optimal member of the population, and the addition of the adaptive step size. These modifications are meant to improve the exploitation abilities, boost the accuracy of the solutions, and balance the search process when dealing with the FS problem in medical datasets. The performance of the proposed algorithm on 12 real medical datasets was compared with the performance of the most effective MA and CMBO variants. The statistical results demonstrated that BMCMBO was more effective than other evaluated methods. In addition, the BMCMBO algorithm was employed to select features and diagnose COVID-19 in a real case study. The proposed algorithm identified healthy and infected COVID-19 correctly samples with an accuracy of 98.4\%, demonstrating its superiority.

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“…44 Feature selection has been considered as the most important step in data mining; however, most of the optimization models do not undergo balanced search. Thus, Fang et al 45 proposed nonlinear binary grasshopper whale optimization algorithm for solving feature extraction problem in high dimensional data. With the proposed method, the datasets are expressed in iteration by computation of fewer features, resulting in high accuracy and fitness.…”

Section: Introductionmentioning

confidence: 99%

Optimization of polydopamine coating process for poly lactic acid‐based 3D printed bone plates using machine learning approaches

Sharma,

Gupta,

Mudgal

et al. 2023

Polymer Engineering & Sci

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The three‐dimensional (3D) printed poly lactic acid (PLA) bone plates lack mechanical strength, resulting in premature failure. Coating these plates with polydopamine (PDM) forms covalent bonds with the PLA molecular structure, enhancing their mechanical properties. The mechanical strength of the coated bone plates is influenced by infill density, submersion time, shaker speed, and coating solution concentration. However, conducting experiments for each parameter value to achieve maximum biomechanical tensile strength (BTS) and biomechanical flexural strength (BFS) is time‐consuming and costly. Overall, the combination of response surface methodology (RSM) and machine learning (ML) enables determination of the best printing parameters, leading to reduced material waste, personalized bone plates tailored to individual anatomy, improved implant fit, and functionality. Moreover, this approach has the potential to reduce the need for additional surgeries and overall costs. To optimize coating parameters, this study employs RSM and ML techniques, including genetic algorithm (GA), particle swarm optimization (PSO), random search optimization (RSO), and differential evolution (DE). Experimental validation of the optimized process parameters and their corresponding fitness values is carried out using both RSM and ML approaches. The results demonstrate that GA has the closest relationship between experimental and fitness values, followed by DE, RSM, PSO, and RSO.Highlights Direct immersion coating of polydopamine on 3D printed PLA bone plates. Evaluating mechanical strength for bone plates coated at varying parameters. Statistical modeling and optimization of mechanical strength using RSM. Mechanical strength optimization and convergence properties for ML models. Experimental validation of RSM and ML‐based optimization algorithms.

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A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection

Cited by 23 publications

References 40 publications

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Optimization of polydopamine coating process for poly lactic acid‐based 3D printed bone plates using machine learning approaches

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