Dual instance and attribute weighting for Naive Bayes classification

Jia Wu, Shirui Pan, Zhihua Cai, Xingquan Zhu, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

10 Citations (Scopus)


Naive Bayes (NB) network is a popular classification technique for data mining and machine learning. Many methods exist to improve the performance of NB by overcoming its primary weakness - the assumption that attributes are conditionally independent given the class, using techniques such as backwards sequential elimination and lazy elimination. Some weighting technologies, including attribute weighting and instance weighting, have also been proposed to improve the accuracy of NB. In this paper, we propose a dual weighted model, namely DWNB, for NB classification. In DWNB, we firstly employ an instance similarity based method to weight each training instance. After that, we build an attribute weighted model based on the new training data, where the calculation of the probability value is based on the embedded instance weights. The dual instance and attribute weighting allows DWNB to tackle the conditional independence assumption for accurate classification. Experiments and comparisons on 36 benchmark data sets demonstrate that DWNB outperforms existing weighted NB algorithms.
Original languageEnglish
Title of host publication2014 International Joint Conference on Neural Networks (IJCNN)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781479914845
Publication statusPublished - 1 Jul 2014
Externally publishedYes
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014


Conference2014 International Joint Conference on Neural Networks, IJCNN 2014


  • Bayes methods
  • data mining
  • directed graphs
  • learning (artificial intelligence)
  • pattern classification
  • naive Bayes classification
  • machine learning
  • dual weighted model
  • DWNB
  • NB classification
  • instance similarity based method
  • training instance
  • attribute weighted model
  • training data
  • probability value
  • embedded instance weights
  • Niobium
  • Training
  • Accuracy
  • Training data
  • Data mining


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