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: firstname.lastname@example.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.