Attribute weighting via differential evolution algorithm for attribute Weighted Naive Bayes (WNB)

Jia Wu, Zhihua Cai

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


The naive Bayes (NB) is a popular classification technique for data mining and machine learning, which is based on the attribute independence assumption. Researchers have proposed out many effective methods to improve the performance of NB by lowering its primary weakness-the assumption that attributes are independent given the class, such as backwards sequential elimination method, lazy elimination method and so on. Recently, Mark Hall presents a simple filter method for setting attribute weights for naive Bayes and proposes a decision tree-based attribute weighted method. In his paper, the experimental results show that the new weighted method performs better than other weighted methods. That weighting idea is taken as the objective of our study in which we use differential evolution algorithms to determine the weights of attributes and then use these weights in our previously developed Weighted Naïve Bayes (WNB). We evaluate the performance of new configuration (DE-WNB) on the whole 36 standard UCI data sets in Weka system. We also compare it with the decision tree-based attribute weighted methods and other methods mentioned in Mark Hall' paper for those data sets. Our experimental results show that the classification accuracy of our new algorithm DE-WNB is much higher than those of the other algorithms used to compare. The obtained classification accuracy is very good with respect to other common WNB classifiers in literature.
Original languageEnglish
Pages (from-to)1672-1679
Number of pages1
JournalJournal of Computational Information Systems
Issue number5
Publication statusPublished - 1 May 2011
Externally publishedYes


  • Naive Bayes
  • Attribute Weighting
  • Differential Evolution
  • Classification

Fingerprint Dive into the research topics of 'Attribute weighting via differential evolution algorithm for attribute Weighted Naive Bayes (WNB)'. Together they form a unique fingerprint.

Cite this