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A Double Weighted Naive Bayes for Multi-label Classification

  • Xuesong Yan
  • Wei Li
  • Qinghua Wu
  • Victor S. Sheng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 575)

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 to determine the weight configuration automatically. Our experimental results show that our proposed DWNB outperforms NB and its extensions significantly in multi-label classification.

Keywords

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

Notes

Acknowledgement

This paper is supported by Natural Science Foundation of China (No. 61402425, 61272470, 61305087, 61440060, 41404076), the Provincial Natural Science Foundation of Hubei (No. 2013CFB320, 2015CFA065).

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Xuesong Yan
    • 1
  • Wei Li
    • 1
  • Qinghua Wu
    • 2
  • Victor S. Sheng
    • 3
  1. 1.School of Computer ScienceChin University of GeoscienceHubeiChina
  2. 2.Faculty of Computer Science and EngineeringWuHan Institute of TechnologyHubeiChina
  3. 3.Department of Computer ScienceUniversity of Central ArkansasConwayUSA

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