  The universal-set naive Bayes classifier (UNB)~\cite{Komiya:13}, defined
using likelihood ratios (LRs), was proposed to address imbalanced
classification problems. However, the LR estimator used in the UNB
overestimates LRs for low-frequency data, degrading the classification
performance. Our previous study~\cite{Kikuchi:19} proposed an effective LR
estimator even for low-frequency data. This estimator uses regularization to
suppress the overestimation, but we did not consider imbalanced data. In this
paper, we integrated the estimator with the UNB. Our experiments with
imbalanced data showed that our proposed classifier effectively adjusts the
classification scores according to the class balance using regularization
parameters and improves the classification performance.
