# Improving Multi-class Classifier Using Likelihood Ratio Estimation with Regularization

arXiv: https://arxiv.org/abs/2210.16033 · HTML: https://mlsanity.com/paper/2210.16033/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Feature Selective Likelihood Ratio Estimator for Low- and Zero-frequency N-grams](https://mlsanity.com/paper/2111.03350/) (2021)

## Abstract (arXiv)

The universal-set naive Bayes classifier (UNB)~citeKomiya: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~citeKikuchi: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.