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Cluster Based Symbolic Representation for Skewed Text Categorization

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 709))

Abstract

In this work, a problem associated with imbalanced text corpora is addressed. A method of converting an imbalanced text corpus into a balanced one is presented. The presented method employs a clustering algorithm for conversion. Initially to avoid curse of dimensionality, an effective representation scheme based on term class relevancy measure is adapted, which drastically reduces the dimension to the number of classes in the corpus. Subsequently, the samples of larger sized classes are grouped into a number of subclasses of smaller sizes to make the entire corpus balanced. Each subclass is then given a single symbolic vector representation by the use of interval valued features. This symbolic representation in addition to being compact helps in reducing the space requirement and also the classification time. The proposed model has been empirically demonstrated for its superiority on bench marking datasets viz., Reuters 21578 and TDT2. Further, it has been compared against several other existing contemporary models including model based on support vector machine. The comparative analysis indicates that the proposed model outperforms the other existing models.

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Acknowledgements

The second author of this paper acknowledges the financial support rendered by the University of Mysore under UPE grants for the High Performance Computing laboratory. The first and fourth authors of this paper acknowledge the financial support rendered by Pillar4 Company, Bangalore.

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Authors and Affiliations

  1. Department of Studies in Computer Science, University of Mysore, Mysore, India

    Lavanya Narayana Raju, Mahamad Suhil, D. S. Guru & Harsha S. Gowda

Authors
  1. Lavanya Narayana Raju
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  2. Mahamad Suhil
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  3. D. S. Guru
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  4. Harsha S. Gowda
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Corresponding author

Correspondence to Mahamad Suhil .

Editor information

Editors and Affiliations

  1. The University of South Dakota, Vermillion, South Dakota, USA

    K.C. Santosh

  2. Karnatak Arts, Science and Commerce College, Bidar, India

    Mallikarjun Hangarge

  3. Polytecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  4. University of Hyderabad, Hyderabad, India

    Atul Negi

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Raju, L.N., Suhil, M., Guru, D.S., Gowda, H.S. (2017). Cluster Based Symbolic Representation for Skewed Text Categorization. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_19

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  • DOI: https://doi.org/10.1007/978-981-10-4859-3_19

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