Home > Archive > 2012 > Volume 2 Number 5 (Oct. 2012) >
IJMLC 2012 Vol.2(5): 588-592 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.195

Boosted Hybrid Recurrent Neural Classifier for Text Document Classification on the Reuters News Text Corpus

Emmanuel Buabin

Abstract—The objective is multi–classed news text classification using hybrid neural techniques on the modapte version of the Reuters news text corpus. In particular, a neuroscience based hybrid neural classifier fully integrated with a novel boosting algorithm is examined for its potential in text document classification in a non-stationary environment. The novel boosting algorithm termed NeuroBoost is an Adaboost-like algorithm that computes and integrates boosted weights into neural network weights, using back-propagation approach. The main contribution of this paper is the provision of an obvious scientific basis for integrating boosted weights into hybrid neural network weights. Results attained in this experiment show impressive performance by the hybrid neural classifier even with minimal number of neurons in constituting structures. A minimal but appreciable increase is observed in performance if an appreciable number of neurons are added.

Index Terms—Hybrid neural classifier, intelligent agent, natural language processing, recurrent neural network, text classification.

E. Buabin is with the Methodist University College Ghana, Dansoman, Greater Accra, Ghana (e-mail: emmanuel.buabin@ieee.org).


Cite:Emmanuel Buabin, "Boosted Hybrid Recurrent Neural Classifier for Text Document Classification on the Reuters News Text Corpus," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 588-592, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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