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Deep neural networks are a family of computational models that are naturallysuited to the analysis of hierarchical data such as, for instance, sequentialdata with the use of recurrent neural networks. In the other hand, ordinalregression is a well-known predictive modelling problem used in fields asdiverse as psychometry to deep neural network based voice modelling. Theirspecificity lies in the properties of their outcome variable, typicallyconsidered as a categorical variable with natural ordering properties,typically allowing comparisons between different states ("a little" is lessthan "somewhat" which is itself less than "a lot", with transitivity allowed).This article investigates the application of sequence-to-sequence learningmethods provided by the deep learning framework in ordinal regression, byformulating the ordinal regression problem as a sequential binary search. Amethod for visualizing the model's explanatory variables according to theordinal target variable is proposed, that bears some similarities to lineardiscriminant analysis. The method is compared to traditional ordinal regressionmethods on a number of benchmark dataset, and is shown to have comparable orsignificantly better predictive power.

Learning a binary search with a recurrent neural network. A novel approach to ordinal regression analysis