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A Double Weighted Naive Bayes for Multi-label Classification

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Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((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.

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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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Authors and Affiliations

  1. School of Computer Science, Chin University of Geoscience, Hubei, 430074, Wuhan, China

    Xuesong Yan & Wei Li

  2. Faculty of Computer Science and Engineering, WuHan Institute of Technology, Hubei, 430074, Wuhan, China

    Qinghua Wu

  3. Department of Computer Science, University of Central Arkansas, Conway, AR, 72035, USA

    Victor S. Sheng

Authors
  1. Xuesong Yan
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  2. Wei Li
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  3. Qinghua Wu
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  4. Victor S. Sheng
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Corresponding author

Correspondence to Xuesong Yan .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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Yan, X., Li, W., Wu, Q., Sheng, V.S. (2016). A Double Weighted Naive Bayes for Multi-label Classification. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_40

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

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