Name
Abstract | ||
|---|---|---|
The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper explores the notion of kernel trick and empirical feature space in order to reformulate the most widely used linear ordinal classification algorithm (the Proportional Odds Model or POM) to perform nonlinear decision regions. The proposed method seems to be competitive with other state-of-the-art algorithms and significantly improves the original POM algorithm when using 8 ordinal datasets. Specifically, the capability of the methodology to handle nonlinear decision regions has been proven by the use of a non-linearly separable toy dataset. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-38679-4_26 | IWANN (1) |
Keywords | Field | DocType |
non-linearly separable toy dataset,proportional odds model,ordinal datasets,linear ordinal classification algorithm,nonlinear decision region,empirical kernel mapping,state-of-the-art algorithm,empirical feature space,ordinal regression,kernel trick,original pom algorithm | Ordered logit,Feature vector,Nonlinear system,Pattern recognition,Computer science,Ordinal number,Ordinal data,Separable space,Ordinal regression,Artificial intelligence,Kernel method,Machine learning | Conference |
Volume | ISSN | Citations |
7902 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Pérez-Ortiz | 1 | 61 | 12.51 |
| Pedro Antonio Gutiérrez | 2 | 433 | 47.30 |
| Manuel Cruz-Ramírez | 3 | 99 | 8.04 |
| Javier Sánchez-Monedero | 4 | 98 | 12.18 |
| César Hervás-Martínez | 5 | 796 | 78.92 |