Performance Comparison of Grid Search and Random Search Methods for Hyperparameter Tuning in Extreme Gradient Boosting Algorithm to Predict Chronic Kidney Failure

doi:10.22266/ijies2021.1231.19

Cited by 20 publications

(9 citation statements)

References 24 publications

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“…Evaluation of this research used the accuracy, precision, recall, and F1-score, as defined by ( 15)- (18), respectively [29]. The training and test portions of the dataset were split by a ratio of 75% to 25%, respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Detection of chronic kidney disease using binary whale optimization algorithm

Sutikno,

Kusumaningrum,

Sugiharto

et al. 2024

IJ-AI

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Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.

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Section: Performance Evaluationmentioning

confidence: 99%

“…According to the test findings, XGBoost has the highest accuracy (98%). Additionally, Anggoro and Mukti [18] adjusted the XGBoost hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

Detection of chronic kidney disease using binary whale optimization algorithm

Sutikno,

Kusumaningrum,

Sugiharto

et al. 2024

IJ-AI

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“…Use a different algorithm if the search space is too big. Random search is faster but does not always guarantee the most significant outcome [49].…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

An effective crop recommendation method using machine learning techniques

2023

IJATEE

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The soil plays a vital role in agriculture, and soil testing serves as the initial step in determining the optimal nutrient levels for cultivating specific crops. Machine learning (ML) classification techniques can leverage soil nutrient data to recommend suitable crops. The Wrapper-PART-Grid approach, which incorporated crop recommendation data to suggest appropriate crops, was introduced in this paper. This hybrid method combined the grid search (GS) method for hyperparameter optimization, wrapper feature selection strategy, and the partial C4.5 decision tree (PART) classifier for crop recommendation. The proposed approach was compared with other ML techniques, including multilayer perceptron (MLP), instance-based learning with parameter k (IBk), C4.5 decision tree (CDT), and reduced error pruning (REP) tree. Evaluation metrics such as true positive rate, false positive rate, precision, recall, F1-score, root mean squared error (RMSE), and mean absolute error (MAE) were employed to assess these models. The suggested method demonstrated superior reliability, accuracy, and effectiveness compared to other ML models for crop advisory purposes. This method attained a remarkable accuracy rate of 99.31%, the highest among all the approaches considered. In this paper, a MLbased crop recommendation technique aimed at assisting farmers in enhancing their knowledge of cultivating appropriate crops. The technique not only seeks to reduce overall wastage but also aims to increase crop yield and improve crop quality.

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“…For instance, under the default parameterization, each GA generation requires the training of 100 ANNs, each trained with thousands of data records. Thus, tuning all NGSA-II hyperparameters (e.g., via grid search [60][61][62]) would require a prohibitive computational effort. Secondly, NSGA-II works as a second-order optimization procedure since it it selects the input variables that feed the ANN training algorithm (the first-order optimization method).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

An Evolutionary Neural Network Approach for Slopes Stability Assessment

Tinoco,

Gomes Correia,

Cortez

et al. 2023

Applied Sciences

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A current big challenge for developed or developing countries is how to keep large-scale transportation infrastructure networks operational under all conditions. Network extensions and budgetary constraints for maintenance purposes are among the main factors that make transportation network management a non-trivial task. On the other hand, the high number of parameters affecting the stability condition of engineered slopes makes their assessment even more complex and difficult to accomplish. Aiming to help achieve the more efficient management of such an important element of modern society, a first attempt at the development of a classification system for rock and soil cuttings, as well as embankments based on visual features, was made in this paper using soft computing algorithms. The achieved results, although interesting, nevertheless have some important limitations to their successful use as auxiliary tools for transportation network management tasks. Accordingly, we carried out new experiments through the combination of modern optimization and soft computing algorithms. Thus, one of the main challenges to overcome is related to the selection of the best set of input features for a feedforward neural network for earthwork hazard category (EHC) identification. We applied a genetic algorithm (GA) for this purpose. Another challenging task is related to the asymmetric distribution of the data (since typically good conditions are much more common than bad ones). To address this question, three training sampling approaches were explored: no resampling, the synthetic minority oversampling technique (SMOTE), and oversampling. Some relevant observations were taken from the optimization process, namely, the identification of which variables are more frequently selected for EHC identification. After finding the most efficient models, a detailed sensitivity analysis was applied over the selected models, allowing us to measure the relative importance of each attribute in EHC identification.

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Performance Comparison of Grid Search and Random Search Methods for Hyperparameter Tuning in Extreme Gradient Boosting Algorithm to Predict Chronic Kidney Failure

Cited by 20 publications

References 24 publications

Detection of chronic kidney disease using binary whale optimization algorithm

Detection of chronic kidney disease using binary whale optimization algorithm

An effective crop recommendation method using machine learning techniques

An Evolutionary Neural Network Approach for Slopes Stability Assessment

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