一种基于改进特征加权的朴素贝叶斯分类算法

he traditional Naive Bayes classification algorithm does not divide the importance degree according to the different feature items, which makes the classification result inaccurate. In order to solve this problem, this paper introduces Jensen-Shannon (JS) divergence and uses JS divergence to express the amount of information provided by the feature terms. Aiming at the deficiency of JS divergence, the paper consider from the three aspects of word frequency, text frequency and inverse category frequency corrected by coefficient of variation, the JS divergence is adjusted and corrected. The weights are introduced into the naive Bayes formula. Compared with other algorithms, it is proved that this method improves the naive Bias classification algorithm effectively. Therefore, compared with other classification algorithms, the performance of naive Bayesian classification algorithm based on JS divergence feature weighting is greatly improved.