A step towards the improvement in the performance of text classification

Shahid Hussain, Muhammad Rafiq Mufti, Muhammad Khalid Sohail, Humaira Afzal, Ghufran Ahmad, Arif Ali Khan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2162-2179
Number of pages18
JournalKSII Transactions on Internet and Information Systems
Issue number4
StatePublished - Apr 30 2019

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications


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