Attribute Weighted Naive Bayes Classifier

The naive Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naive Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naive Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naive Bayes model and some at the expense of computational time. One approach to reduce the naivete of the classifier is to incorporate attribute weights in the conditional probability. In this paper, we proposed a method to incorporate attribute weights to naive Bayes. To evaluate the performance of our method, we used the public benchmark datasets. We compared our method with the standard naive Bayes and baseline attribute weighting methods. Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naive Bayes and baseline attribute weighting methods in terms of classification accuracy and F1, especially when the independence assumption is strongly violated, which was validated using the Chi-square test of independence.

Automated summary

The naive Bayes classifier is a simple yet effective method for data mining classification. However, the assumption of attribute independence may not hold in real-world applications. To address this, researchers proposed a method to incorporate attribute weights into naive Bayes, which resulted in improved classification performance in terms of accuracy and F1 score.