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New Rough Set-Aided Mechanism for Text Categorisation

Author

Listed:
  • N. Venkata Sailaja

    (Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad 500090, Telangana, India)

  • L. Padma Sree

    (Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad 500090, Telangana, India)

  • N. Mangathayaru

    (Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad 500090, Telangana, India)

Abstract

With the advent of computers and the information age, statistical and analytical problems have grown in terms of both size and complexity. Challenges in core domains of data storage, organisation and searching have evolved to the new research field called data mining. Text classification using various machine learning (ML) mechanisms encounters the difficulty of the high dimensionality of attributes vector. Therefore, a feature selection technique is very much required to discard irrelevant as well as noisy attributes from the feature set vector so that the ML algorithms can work efficiently. In this paper, rough set theory (RST)-based attribute selection methodology is applied to achieve text classification goal. A hybrid method based on RST is proposed for text documents classification. Further, the proposed method’s performance is evaluated on standard datasets.

Suggested Citation

  • N. Venkata Sailaja & L. Padma Sree & N. Mangathayaru, 2018. "New Rough Set-Aided Mechanism for Text Categorisation," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-19, June.
    • Handle: RePEc:wsi:jikmxx:v:17:y:2018:i:02:n:s0219649218500223
    DOI: 10.1142/S0219649218500223
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    References listed on IDEAS

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    1. Berry, Michael W. & Browne, Murray & Langville, Amy N. & Pauca, V. Paul & Plemmons, Robert J., 2007. "Algorithms and applications for approximate nonnegative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 155-173, September.
    2. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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