An improved naive Bayesian classification method based on symmetric uncertainty
Denghui Zhu, Lizhong Song, Wenqiang Yu · Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022) · 2022
According to the harsh condition of the conditional independence of feature attributes in the Naive Bayesian algorithm, an improved Naive Bayesian classification method based on feature correlation weighting is proposed. The degree of correlation between feature attributes, class attributes and feature attributes is judged by symmetric uncertainty, irrelevant feature attributes and redundant attributes are eliminated, and then the correlation between feature attributes and class variables is high and the correlation degree between feature attributes is low. The basic principle of the weight assignment of feature attributes, and finally experimental verification on the public data set. According to the experimental results, the algorithm can effectively reduce the influence of the correlation between the features and effectively improve the classification accuracy.