Journal
NEUROCOMPUTING
Volume 401, Issue -, Pages 48-58Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2020.03.034
Keywords
Deep learning; Ordinal regression; Cumulative link models; Kappa index
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Funding
- Spanish Ministry of Economy and Competitiveness (MINECO) [TIN2017-85887C2-1-P]
- FEDER funds of the European Union [TIN2017-85887C2-1-P]
- Ministry of Economy, Knowledge, Business and University of the Junta de Andalucia [UCO-1261651]
- FPU Predoctoral Program of the Spanish Ministry of Science, Innovation and Universities (MCIU) [FPU18/00358]
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This paper proposes a deep convolutional neural network model for ordinal regression by considering a family of probabilistic ordinal link functions in the output layer. The link functions are those used for cumulative link models, which are traditional statistical linear models based on projecting each pattern into a 1-dimensional space. A set of ordered thresholds splits this space into the different classes of the problem. In our case, the projections are estimated by a non-linear deep neural network. To further improve the results, we combine these ordinal models with a loss function that takes into account the distance between the categories, based on the weighted Kappa index. Three different link functions are studied in the experimental study, and the results are contrasted with a statistical analysis. The experiments run over two different ordinal classification problems and the statistical tests confirm that these models improve the results of a nominal model and outperform other robust proposals considered in the literature. (C) 2020 Elsevier B.V. All rights reserved.
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