Abstract
The aim of this paper is to analyze the potentialities of Bidirectional Recurrent Neural Networks in classification problems. Different functions are proposed to merge the network outputs into one single classification decision. In order to analyze when these networks could be useful; artificial datasets were constructed to compare their performance against well-known classification methods in different situations, such as complex and simple decision boundaries, and related and independent features. The advantage of this neural network in classification problems with complicated decision boundaries and feature relations was proved statistically. Finally, better results using this network topology in the prediction of HIV drug resistance were also obtained.
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