基于KL距离的TAN分类器判别性学习方法

Discriminative Learning of TAN Classifier Based on KL Divergence

树增强朴素贝叶斯(TAN)分类器在模型的复杂性和分类精度之间实现较好折衷,成为当前分类器学习的一个研究热点.为了提高 TAN 分类器的分类准确率,本文提出一种基于 KL 距离的 TAN 分类器判别性学习方法.首先用 EAR 方法学习 TAN 分类器的结构,然后用基于 KL 距离的目标函数优化 TAN 的参数.在标准数据集上的实验结果表明,用该方法学习的 TAN 分类器具有较高的分类精度.

Tree-augmented Naive bayes （TAN） classifier is a compromise between model complexity and classification rate. It is a hot research topic currently. To improve the classification accuracy of TAN classifier, a discriminative method based on Kullback-Leibler （KL） divergence is proposed. Explaining away residual （EAR） method is used to learn the structure of TAN, and then the TAN parameters are obtained by an objective function based on KL divergence. The experimental results on benchmark datasets show that the proposed method can get relatively high classification rates.