Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

Elgeldawi, Enas; Sayed, Awny; Galal, Ahmed R.; Zaki, Alaa M.

doi:10.3390/informatics8040079

Cited by 227 publications

(98 citation statements)

References 38 publications

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“…The primary hyperparameters of the K-NN model (the number of neighbors' k and the similarity function or the distance metric) are tuned to get the optimal results [ 30 , 38 , 39 ].…”

Section: Results and Discussion Of Optimized Risk Modelsmentioning

confidence: 99%

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Ansarullah¹,

Saif²,

Andrabi

et al. 2022

Journal of Healthcare Engineering

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Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. The multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. The assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. The enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclassification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. The model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial prediction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease.

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“…The primary hyperparameters of the K-NN model (the number of neighbors' k and the similarity function or the distance metric) are tuned to get the optimal results [ 30 , 38 , 39 ].…”

Section: Results and Discussion Of Optimized Risk Modelsmentioning

confidence: 99%

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Ansarullah¹,

Saif²,

Andrabi

et al. 2022

Journal of Healthcare Engineering

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“…Kumari et al [14] in their paper "An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier" proposed an ensemble soft voting classifier for predicting diabetes mellitus using three machine learning algorithms: Logistic Regression, Random Forest, and Naive Bayes for the classification. Elgeldawi et al [12] in their paper "Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis" suggest that the choice and settings of a machine learning model's hyperparameters can have a significant impact on the model's performance. Ngoc et al [15] in their paper "Hyperparameter Optimization in Classification: To-do or Not-todo" propose a framework for deciding whether to use hyperparameter optimization or the default hyper-parameter settings when dealing with the problem of whether or not to apply hyper-parameter optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the negative impacts posed by fake news can be mitigated to the barest minimum by several state-of-the-art machine learning algorithms. Machine learning algorithms have become so popular that they are now used in practically every scientific field [12]. In this paper, we offer a better method for classifying news articles as fake or real based on their content using the ensemble machine learning technique.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Machine Learning Approach for Fake News Detection and Classification Using a Soft Voting Classifier

Lasotte

Garba

Malgwi

et al. 2022

EJECE

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Fake news has grown in popularity and spread as a result of increased insecurity, political events, and pandemics, among other things. This study used an ensemble machine learning technique to better predict fake news on social media based on the content of news articles. The proposed model used a soft voting classifier to aggregate four machine learning algorithms, namely, Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression, for the classification of news articles as fake or real. GridSearchCV was used to fine-tune the algorithms to get the optimal results during the training process. A Kaggle dataset was used for the experiment; it was comprised of both false and true news. Performance evaluation metrics were used to measure the performance of the base learners and our proposed ensemble technique on the dataset. The results of our experiment show that the proposed ensemble approach produced the highest accuracy, precision, recall, and F1_score values of 93%, 94%, 92%, and 93%, respectively, on the dataset as compared to the individual learners. This approach may also be used in other classification techniques for spam detection, sentiment analysis, and prediction of loan eligibility, among other things.

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“…Although studies have shown several approaches for optimizing hyperparameter values, grid search and random search have always been considered the most straightforward algorithms to implement. This is because grid search tries all possible value combinations [27], whereas random search randomly combines different values [28]. However, one of the disadvantages of grid search is that it is computationally expensive on large datasets [27].…”

mentioning

confidence: 99%

“…However, one of the disadvantages of grid search is that it is computationally expensive on large datasets [27]. On the other hand, the random search is not exhaustive, where the randomly combined values were chosen without any strategy or prior trial information [28]. Another common approach is Bayesian optimization.…”

mentioning

confidence: 99%

Machine learning model for malaria risk prediction based on mutation location of large-scale genetic variation data

Tai

Dhaliwal

2022

J Big Data

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In recent malaria research, the complexity of the disease has been explored using machine learning models via blood smear images, environmental, and even RNA-Seq data. However, a machine learning model based on genetic variation data is still required to fully explore individual malaria risk. Furthermore, many Genome-Wide Associations Studies (GWAS) have associated specific genetic markers, i.e., single nucleotide polymorphisms (SNPs), with malaria. Thus, the present study improves the current state-of-the-art genetic risk score by incorporating SNPs mutation location on large-scale genetic variation data obtained from GWAS. Nevertheless, it becomes computationally expensive for hyperparameter optimization on large-scale datasets. Therefore, this study proposes a machine learning model that incorporates mutation location as well as a Genetic Algorithm (GA) to optimize hyperparameters. Besides that, a deep learning model is also proposed to predict individual malaria risk as an alternative approach. The analysis is performed on the Malaria Genomic Epidemiology Network (MalariaGEN) dataset comprising 20,817 individuals from 11 populations. The findings of this study demonstrated that the proposed GA could overcome the curse of dimensionality and improve resource efficiency compared to commonly used methods. In addition, incorporating the mutation location significantly improved the machine learning models in predicting the individual malaria risk; a Mean Absolute Error (MAE) score of 8.00E−06. Moreover, the deep learning model obtained almost similar MAE scores to the machine learning models, indicating an alternative approach. Thus, this study provides relevant knowledge of genetic and technical deliberations that can improve the state-of-the-art methods for predicting individual malaria risk.

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Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

Cited by 227 publications

References 38 publications

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

An Ensemble Machine Learning Approach for Fake News Detection and Classification Using a Soft Voting Classifier

Machine learning model for malaria risk prediction based on mutation location of large-scale genetic variation data

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