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Addressing Class Imbalance in Bayesian Classification Through Posterior Probability Adjustment
Vahid Nassiri1, Fetene Tekle2, Kanaka Tatikola2,3
1Open Analytics, Antwerp, Belgium.
Abstract:
Class imbalance is a known issue in classification tasks that can lead to predictive bias toward dominant classes. This paper introduces a novel straightforward Bayesian framework that adjusts posterior probabilities to counteract the bias introduced by imbalanced data sets. Instead of relying on the mean posterior distribution of class probabilities, we propose a method that scales the posterior probability of each class according to their representation in the training data.
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