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Text Classification Using Ensemble Features Selection and Data Mining Techniques

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Book cover Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

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Abstract

Text categorization is a task of text mining/analytics which involves extracting useful information from unstructured resources followed by categorizing these documents. In this paper, we classify the TechTC dataset collected from various Web directories. We employed feature selection methods such as Gini index, chi-square, t-statistic, correlation which drastically reduced the model building time. Various neural network models such as probabilistic neural network, group method of data handling, multi layer perceptron yielded higher accuracies compared to other techniques applied in literature.

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Author information

Authors and Affiliations

  1. Centre of Excellence in CRM and Analytics, Institute for Development and Research in Banking Technology, Castle Hills Road no 1, Masab Tank, Hyderabad, 500 057, Andhra Pradesh, India

    B. Shravankumar & Vadlamani Ravi

  2. SCIS, University of Hyderabad, Hyderabad, 500 046, Andhra Pradesh, India

    B. Shravankumar

Authors
  1. B. Shravankumar
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  2. Vadlamani Ravi
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Correspondence to Vadlamani Ravi .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Shravankumar, B., Ravi, V. (2015). Text Classification Using Ensemble Features Selection and Data Mining Techniques. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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