Abstract: In highly imbalanced binary classification tasks with asymmetric misclassification costs, traditional cost-insensitive learning strategies fail to reflect true risk and often yield poor ...
Objective: This study aimed to determine optimal sample sizes and the relationships between sample size and dataset-level characteristics over a variety of binary classification algorithms. Methods: A ...
Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
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