A double weighted Naive Bayes for multi-label classification

Xuesong Yan, Wei Li, Qinghua Wu, Victor S. Sheng

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

6 Scopus citations


Multi-label classification is to assign an instance to multiple classes. Naïve Bayes (NB) is one of the most popular algorithms for pattern recognition and classification. It has a high performance in single label classification. It is naturally extended for multi-label classification under the assumption of label independence. As we know, NB is based on a simple but unrealistic assumption that attributes are conditionally independent given the class. Therefore, a double weighted NB (DWNB) is proposed to demonstrate the influences of predicting different labels based on different attributes. Our DWNB utilizes the niching cultural algorithm to determine the weight configuration automatically. Our experimental results show that our proposed DWNB outperforms NB and its extensions significantly in multi-label classification.

Original languageEnglish
Title of host publicationComputational Intelligence and Intelligent Systems - 7th International Symposium, ISICA 2015, Revised Selected Papers
EditorsKangshun Li, Jin Li, Yong Liu, Aniello Castiglione
Number of pages8
ISBN (Print)9789811003554
StatePublished - 2016
Event7th International Symposium on Computational Intelligence and Intelligent Systems, ISICA 2015 - Guangzhou, China
Duration: Nov 21 2015Nov 22 2015

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference7th International Symposium on Computational Intelligence and Intelligent Systems, ISICA 2015


  • Cultural algorithm
  • Double weighted Naive Bayes
  • Multi-label classification
  • Naive Bayes


Dive into the research topics of 'A double weighted Naive Bayes for multi-label classification'. Together they form a unique fingerprint.

Cite this