A Double Weighted Naive Bayes with Niching Cultural Algorithm for Multi-Label Classification

Xuesong Yan, Qinghua Wu, Victor S. Sheng

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Multi-label classification is to assign an instance to multiple classes. Naive 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 (NLA) 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
Article number1650013
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume30
Issue number6
DOIs
StatePublished - Jul 1 2016

Keywords

  • Multi-label classification
  • Naive Bayes
  • cultural algorithm
  • double weighted naive bayes
  • niching cultural algorithm

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