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An Extended Random-Sets Model for Fusion-Based Text Feature Selection

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Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10939))

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Abstract

Selecting features that represent a specific corpus is important for the success of many machine learning and text mining applications. In information retrieval (IR), fusion-based techniques have shown remarkable performance compared to traditional models. However, in text feature selection (FS), popular models do not consider the fusion of the taxonomic features of the corpus. This research proposed an innovative and effective extended random-sets model for fusion-based FS. The model fused scores of different hierarchal features to accurately weight the representative words based on their appearance across the documents in the corpus and in several latent topics. The model was evaluated for information filtering (IF) using TREC topics and the standard RCV1 dataset. The results showed that the proposed model significantly outperformed eleven state-of-the-art baseline models in six evaluation metrics.

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Notes

  1. 1.

    Words, keywords and terms are used interchangeably in this paper.

  2. 2.

    SIF stands for Selection of Informative Features, and the ‘2’ refers to the utilisation of both local and global statistics.

  3. 3.

    http://trec.nist.gov/.

  4. 4.

    https://www.lemurproject.org/.

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Author information

Authors and Affiliations

  1. School of EECS, Queensland University of Technology, Brisbane, QLD, Australia

    Abdullah Semran Alharbi, Yuefeng Li & Yue Xu

  2. Department of CS, Umm Al-Qura University, Mecca, Saudi Arabia

    Abdullah Semran Alharbi

Authors
  1. Abdullah Semran Alharbi
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  2. Yuefeng Li
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  3. Yue Xu
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Corresponding author

Correspondence to Abdullah Semran Alharbi .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

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Alharbi, A.S., Li, Y., Xu, Y. (2018). An Extended Random-Sets Model for Fusion-Based Text Feature Selection. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-93040-4_11

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  • Online ISBN: 978-3-319-93040-4

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