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A Step towards the Improvement in the Performance of Text Classification
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Step towards the Improvement in the Performance of Text Classification


Abstract

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier’s performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.


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Cite this article

[IEEE Style]
S. Hussain, M. R. Mufti, M. K. Sohail, H. Afzal, G. Ahmad and A. A. Khan, "A Step towards the Improvement in the Performance of Text Classification," KSII Transactions on Internet and Information Systems, vol. 13, no. 4, pp. 2162-2179, 2019. DOI: 10.3837/tiis.2019.04.024.

[ACM Style]
Shahid Hussain, Muhammad Rafiq Mufti, Muhammad Khalid Sohail, Humaira Afzal, Ghufran Ahmad, and Arif Ali Khan. 2019. A Step towards the Improvement in the Performance of Text Classification. KSII Transactions on Internet and Information Systems, 13, 4, (2019), 2162-2179. DOI: 10.3837/tiis.2019.04.024.