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Mitigating false negatives in imbalanced datasets: an ensemble approach
Cavique, Luís; Vasconcelos, MarceloImbalanced datasets present a challenge in machine learning, especially in binary classification scenarios where one class significantly outweighs the other. This imbalance often leads to models favoring the majority class, resulting in inadequate predictions for the minority class, specifically in false negatives. In response to this issue, this work introduces the MinFNR ensemble algorithm, designed to minimi...
Data: 2025
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Origem: Repositório Aberto da Universidade Aberta