论文标题
使用正则化使用似然比估计来改善多级分类器
Improving Multi-class Classifier Using Likelihood Ratio Estimation with Regularization
论文作者
论文摘要
提出了使用似然比（LRS）定义的通用天真贝叶斯分类器（UNB）〜\ cite {komiya：13}来解决分类不平衡问题。但是，UNB中使用的LR估计器高估LRs以用于低频数据，从而降低了分类性能。我们先前的研究〜\ cite {kikuchi：19}甚至针对低频数据提出了有效的LR估计器。该估计器使用正则化来抑制高估，但我们没有考虑数据不平衡。在本文中，我们将估算器与UNC集成在一起。我们对数据不平衡的实验表明，我们提出的分类器使用正则化参数有效地根据班级平衡来调整分类得分，并改善分类性能。
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.
