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An Improved Multi-classification Algorithm for Imbalanced Online Public Opinion Data

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Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11635))

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Abstract

When the datasets about online public opinion are imbalanced, the classifier is prone to sacrifice the accuracy of minority class to achieve the overall best performance. In order to solve this problem, an online public opinion text multi-classification algorithm based on random forest and cost-sensitive is proposed in this essay. The algorithm uses Naïve Bayes to construct cost matrix, chooses Gini index with misclassification cost to select the decision tree node. After the comparative experiment, the classifier has improved performance by 3% overall and 8% on minority classes, which can solve the problem of data imbalance to some extent.

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Acknowledgements

This work is supported by the National Key Research and Development Plan (Grant No. 2017YFC0820603), BUPT’s Graduate education reform project (2018Y003) and the Project of Chinese Society of Academic degrees and graduate education (2017Y0502).

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Authors and Affiliations

  1. Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China

    Xige Dang, Xu Wu & Xiaqing Xie

  2. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China

    Xige Dang, Xu Wu & Xiaqing Xie

  3. Beijing University of Posts and Telecommunications Library, Beijing, China

    Xu Wu

  4. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

    Tianle Zhang

Authors
  1. Xige Dang
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  2. Xu Wu
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  3. Xiaqing Xie
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  4. Tianle Zhang
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Corresponding author

Correspondence to Xu Wu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Dang, X., Wu, X., Xie, X., Zhang, T. (2019). An Improved Multi-classification Algorithm for Imbalanced Online Public Opinion Data. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11635. Springer, Cham. https://doi.org/10.1007/978-3-030-24268-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-24268-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24267-1

  • Online ISBN: 978-3-030-24268-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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