Name
Abstract | ||
|---|---|---|
This paper proposes a deep neural network model for ordinal regression problems based on the use of a probabilistic ordinal link function in the output layer. This link function reproduces the Proportional Odds Model (POM), a statistical linear model which projects each pattern into a 1-dimensional space. In our case, the projection is estimated by a non-linear deep neural network. After that, patterns are classified using a set of ordered thresholds. In order to further improve the results, we combine this link function with a loss cost that takes the distance between classes into account, based on the weighted Kappa index. The experiments are based on two ordinal classification problems, and the statistical tests confirm that our ordinal network outperforms the nominal version and other proposals considered in the literature. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-19651-6_43 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Ordinal regression,Ordinal classification,Proportional odds model,Deep learning,Convolutional neural network | Ordered logit,Ordinal number,Computer science,Convolutional neural network,Linear model,Ordinal regression,Artificial intelligence,Deep learning,Probabilistic logic,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
11487 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Víctor Manuel Vargas | 1 | 6 | 1.78 |
| Pedro Antonio Gutiérrez | 2 | 433 | 47.30 |
| César Hervás | 3 | 183 | 14.38 |