一种贝叶斯网络分类器集群式参数学习的降噪算法

文中首先分析降噪集成算法采用的样本置信度度量函数的性质，阐述此函数不适合处理多类问题的根源。进而设计更有针对性的置信度度量函数，并基于此函数提出一种增强型降噪参数集成算法。从而使鉴别式贝叶斯网络参数学习算法不但有效地抑止噪声影响，而且避免分类器的过度拟合，进一步拓展采用集群式学习算法的鉴别式贝叶斯网络分类器在多类问题上的应用。最后，实验结果及其统计假设检验分析充分验证此算法比目前的集群式贝叶斯网络参数学习方法得到的分类器在性能上有较显著提高。

A Smoothed Boosting Algorithm for Ensemble Parameters Learning of Bayesian Network Classifiers

The property of sample confidence measure function applied by ensemble algorithm of reducing noises is firstly analysed in this paper, and the reason of this function being unfit for multiclass dataset is expounded. Then a confidence measure function with more pertinence is designed, and an enhanced algorithm for reducing noises and ensemble parameters is proposed based on this function. Thus the discriminative parameters learning algorithm of Bayesian network not only effectively restrains the noise impact, but also avoids over fitting of classifiers, and further extend the application of discriminative Bayesian network calssifier applying ensemble learning algorithm in multiclass problem. Finally, the experimental results and its analysis on statistical hypothesis test verify that this algorithm more notably improves the classifier performance than ensemble parameters learning algorithms of Bayesian network at present.