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Imbalanced Chinese Text Classification Based on Weighted Sampling

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Trustworthy Computing and Services (ISCTCS 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 426))

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Abstract

Traditional text classification methods assume that dataset is balanced. But, in the real world, there are plenty of imbalanced data on which traditional classification methods could not get satisfactory results. Based on comprehensive analysis of existing researches on imbalanced data classification, we propose a data rebalance method based on weighted sampling. The method assigns weights to each class by calculating the ratio between different categories. Then, each class is sampled with different ratios using weighted sampling methods. Experimental results on real Chinese text data set show that the proposed method can effectively improve the classification accuracy.

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Notes

  1. 1.

    The corpus is available at http://www.nlpir.org/download.

  2. 2.

    http://www.ictclas.org

  3. 3.

    http://weka.wikispaces.com

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Acknowledgements

The authors’ work was sponsored by National Program on Key Basic Research Project (973 Program) of China (2013CB329601, 2013CB329602), National High Technology Research and Development Program (863 Program) of China (2011AA010702, 2012AA01A401 and 2012AA01A402), the Nature Science Foundation of China (60933005, 91124002), Support Science and Technology Project of China (2012BAH38B04, 2012BAH38B06), China Postdoctoral Science Foundation Program (2012M520114).

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, Hunan Province, People’s Republic of China

    Hu Li, Peng Zou, WeiHong Han & Rongze Xia

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  1. Hu Li
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  2. Peng Zou
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  3. WeiHong Han
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  4. Rongze Xia
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Corresponding author

Correspondence to Hu Li .

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Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Yuyu Yuan

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xu Wu

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

    Yueming Lu

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Li, H., Zou, P., Han, W., Xia, R. (2014). Imbalanced Chinese Text Classification Based on Weighted Sampling. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2013. Communications in Computer and Information Science, vol 426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43908-1_5

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  • DOI: https://doi.org/10.1007/978-3-662-43908-1_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43907-4

  • Online ISBN: 978-3-662-43908-1

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