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

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.