Addressing Class Imbalance in Bayesian Classification Through Posterior Probability Adjustment
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.
Conflicts of Interest
The authors declare no conflicts of interest.
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The data that support the findings of this study are available in the Supporting Information of this article.
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