Search alternatives:
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
basis function » loss function (Expand Search), brain function (Expand Search), barrier function (Expand Search)
binary basis » binary mask (Expand Search), binary pairs (Expand Search)
used model » based model (Expand Search), based models (Expand Search)
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
basis function » loss function (Expand Search), brain function (Expand Search), barrier function (Expand Search)
binary basis » binary mask (Expand Search), binary pairs (Expand Search)
used model » based model (Expand Search), based models (Expand Search)
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61
ROC curve for binary classification.
Published 2024“…The model further showed superior results on binary classification compared with existing methods. …”
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62
Confusion matrix for binary classification.
Published 2024“…The model further showed superior results on binary classification compared with existing methods. …”
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63
ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
Published 2025Subjects: -
64
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65
The Pseudo-Code of the IRBMO Algorithm.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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66
QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm
Published 2020“…The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”
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67
<i>hi</i>PRS algorithm process flow.
Published 2023“…This leads to a set of predictive, yet diverse, interactions that <b>(F)</b> we use to define the score weighting their contribution by fitting a LR model and retaining the corresponding <i>β</i> coefficients.…”
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68
The statistical description of the original data set of the patients (<i>n</i> = 162).
Published 2025Subjects: -
69
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70
The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
Published 2025Subjects: -
71
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72
Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data
Published 2022“…In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. …”
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73
Results of KNN.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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74
Comparison of key techniques in their literature.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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75
SHAP analysis mean value.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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76
Proposed methodology.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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77
SHAP analysis.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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78
Dataset description.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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79
Results of Extra tree.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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80
Results of Decision tree.
Published 2024“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”