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.
翻译:使用概率比率(LRs)定义的通用天天天白贝亚分类器(UNB) ⁇ cite{Komiya:13})被提议解决不平衡的分类问题。然而,UNB中高估低频数据的LRs估计值,降低了分类性能。我们先前的研究“cite{Kikuchi:19}提议了一个有效的LRS估计值,即使低频数据也是如此。这个估计值利用正规化来抑制高估,但我们没有考虑不平衡的数据。在本文中,我们把估计值与UNB合并。我们用不平衡数据进行的实验表明,我们提议的分类员利用正规化参数有效地调整了分类的分数,并改进了分类性能。