The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.

The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.

<p>The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.</p>

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Bibliographic Details
Main Author: Javad Hemmatian (20700209) (author)
Other Authors: Rassoul Hajizadeh (20700212) (author), Fakhroddin Nazari (20700215) (author)
Published: 2025
Subjects:
Medicine
Science Policy
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
using five metrics
oversampling minority classes
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maternal health risk
experimental findings indicate
enn methods across
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become increasingly prominent
achieving average improvements
smote consistently outperformed
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smote outperformed rn
four imbalanced datasets
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imbalanced data
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two clusters
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study proposes
results demonstrate
recent years
proposed method
novel data
machine learning
correlation coefficient
classification algorithms
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