基于属性加权核密度估计的朴素贝叶斯分类算法

A naive Bayes classification algorithm based on attribute weighting and kernel density estimation

为了削弱朴素贝叶斯分类算法的属性条件独立性假设,提出了一种属性加权核密度估计的朴素贝叶斯分类算法。该算法结合条件属性与决策属性的相关系数以及互信息得到新的属性加权值,并将该加权值嵌入核密度估计的朴素贝叶斯分类算法。实验结果表明,该算法提高了分类准确率。

In order to weaken the attribute conditional independence assumption in the naive Bayes classification algorithm,a naive Bayes classification algorithm based on attribute weighting and kernel density estimation is presented.Combining the correlation coefficients of conditional attributes and decision attributes with mutual information,a new attribute weighting is obtained.Then the weighting is embedded into the naive Bayes classification algorithm based on kernel density estimation.Experimental results show that the classification accuracy is improved by the proposed algorithm.