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An Improved Information Gain Algorithm Based on Relative Document Frequency Distribution

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Natural Language Understanding and Intelligent Applications (ICCPOL 2016, NLPCC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10102))

Abstract

Feature selection algorithm plays an important role in text categorization. Considering some drawbacks proposed from traditional and recently improved information gain (IG) approach, an improved IG feature selection method based on relative document frequency distribution is proposed, which combines reducing the impact of unbalanced data sets and low-frequency characteristics, the frequency distribution of features within category and the relative frequency document distribution of features among different categories. The experimental results of NLPCC-ICCPOL 2016 stance detection in Chinese microblogs show that the performance of the improved method is better than traditional IG approach and another improved method in feature selection.

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Acknowledgements

This research work is supported by National Natural Science Foundation of China (No. 61402220, No. 61502221), the Scientific Research Fund of Hunan Provincial Education Department (No. 14B153, No. 16C1378), the Philosophy and Social Science Foundation of Hunan Province (No. 14YBA335).

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Authors and Affiliations

  1. School of Computer Science and Technology, University of South China, Hengyang, 421001, China

    Jian Peng, Xiao-Hua Yang, Chun-Ping Ouyang & Yong-Bin Liu

Authors
  1. Jian Peng
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  2. Xiao-Hua Yang
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  3. Chun-Ping Ouyang
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  4. Yong-Bin Liu
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Corresponding author

Correspondence to Xiao-Hua Yang .

Editor information

Editors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Chin-Yew Lin

  2. Brandeis University, Waltham, Massachusetts, USA

    Nianwen Xue

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Fudan University, Shanghai, China

    Xuanjing Huang

  5. Peking University, Beijing, China

    Yansong Feng

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Peng, J., Yang, XH., Ouyang, CP., Liu, YB. (2016). An Improved Information Gain Algorithm Based on Relative Document Frequency Distribution. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_49

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  • DOI: https://doi.org/10.1007/978-3-319-50496-4_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50495-7

  • Online ISBN: 978-3-319-50496-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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