Attribute Weighted Naïve Bayes Classifier  

作　　者：Lee-Kien Foo Sook-Ling Chua Neveen Ibrahim 

机构地区：[1]Multimedia University,Cyberjaya,63100,Malaysia

出　　处：《Computers, Materials & Continua》2022年第4期1945-1957,共13页计算机、材料和连续体（英文）

摘　　要：The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve 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 naïve Bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve Bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve Bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve 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.

关 键 词：Attribute weighting naïve Bayes Kullback-Leibler information gain CLASSIFICATION 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]