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A Filter Based Feature Selection for Imbalanced Text Classification

  • K. SwarnalathaEmail author
  • D. S. Guru
  • Basavaraj S. Anami
  • N. Vinay Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

In this work, a text classification method through a filter type feature selection for imbalanced data is addressed. The model initially clusters the documents associated with a class through a hierarchical clustering there by accomplishing a balanced or near balanced class. Later, a filter type feature selection is recommended to choose the most discriminative features for text classification. Subsequently, the documents are stored in the form of interval valued data. For classification purpose, a suitable symbolic classifier is recommended. The experimentation is done with two standard benchmarking datasets viz., Reuters 21578 and TDT2. The experimental results obtained from the proposed model are better in terms of f-measure when compared to the available models.

Keywords

Imbalance text Clustering Feature selection Symbolic representation Text classification 

Notes

Acknowledgement

The author N Vinay Kumar acknowledges the Department of Science and Technology, Govt. of India for their financial support rendered through DST-INSPIRE fellowship.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • K. Swarnalatha
    • 1
    Email author
  • D. S. Guru
    • 2
  • Basavaraj S. Anami
    • 3
  • N. Vinay Kumar
    • 2
  1. 1.Department of Information Science and EngineeringMaharaja Institute of Technology ThandavapuraMysuruIndia
  2. 2.Department of Studies in Computer ScienceUniversity of MysoreMysuruIndia
  3. 3.KLE Institute of TechnologyHubliIndia

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