Search alternatives:
based optimization » whale optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
binary case » binary mask (Expand Search), binary image (Expand Search), primary case (Expand Search)
layer model » water model (Expand Search), linear model (Expand Search), cancer model (Expand Search)
case based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
based optimization » whale optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
binary case » binary mask (Expand Search), binary image (Expand Search), primary case (Expand Search)
layer model » water model (Expand Search), linear model (Expand Search), cancer model (Expand Search)
case based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
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Technical approach.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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Data set presentation.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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46
Pearson correlation coefficient matrix plot.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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47
SHAP of stacking.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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48
Demonstration of data imbalance.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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49
Stacking ROC curve chart.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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50
Confusion matrix.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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51
GA-XGBoost feature importances.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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52
Partial results of the chi-square test.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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53
Stacking confusion matrix.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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54
Stacking schematic diagram.
Published 2024“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”
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55
PRNET [6].
Published 2024“…Our proposed algorithm identifies the optimal layer replication configuration for the model. …”
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56
Example of horizontal flipping.
Published 2024“…Our proposed algorithm identifies the optimal layer replication configuration for the model. …”
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57
Example of extending the block (1).
Published 2024“…Our proposed algorithm identifies the optimal layer replication configuration for the model. …”
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58
Cropping and aligning image.
Published 2024“…Our proposed algorithm identifies the optimal layer replication configuration for the model. …”
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59
Example of extending the block (2).
Published 2024“…Our proposed algorithm identifies the optimal layer replication configuration for the model. …”
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Example of weight transferring.
Published 2024“…Our proposed algorithm identifies the optimal layer replication configuration for the model. …”