Improving Multi-class Classifier Using Likelihood Ratio Estimation with Regularization
This work addresses imbalanced classification issues, but it is incremental as it builds on prior methods.
The paper tackled the problem of overestimating likelihood ratios in the universal-set naive Bayes classifier for imbalanced multi-class classification by integrating a regularized estimator, resulting in improved classification performance as shown in experiments with imbalanced data.
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.