An Improved De-noising Algorithm for Bayesian Network Classifiers Parameter Learning

Abstract

The paper first analyzed the property of sample confidence measure function applied by noise reduction algorithm, explained the reason of this function being not suitable for multi-class problems. Then a more targeted confidence measure function was designed, and based on this function, an enhanced de-noise algorithm of ensemble parameters learning was proposed. Thus the discriminative learning algorithm not only effectively restrain the noise, but also avoid the overfitting of the classifier. Finally, the experimental results and statistical analysis for hypothesis testing verified that the current ensemble parameters learning algorithms of Bayesian network was improved obviously in the performance.