Projection based ensemble learning for ordinal regression

Áreas de investigación:

• Methodology - Machine learning

Año:

2014

Tipo de publicación:

Artículo

Palabras clave:

Ordinal regression, ordinal classification, en- semble, threshold models, discriminant analysis, support vector machines, logistic regression, ordinal decomposition

Autores:

• Pérez-Ortiz, María
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Journal:

IEEE Transactions on Cybernetics

Volumen:

44

Número:

5

Páginas:

681-694

Mes:

Mayo

ISSN:

2168-2267

BibTex:

@article{ELOR2013, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper proposes an ensemble methodology specifically adapted to this type of problems, which is based on computing different classification tasks through the formulation of different order hypotheses. Every single model is trained in order to distinguish between one given class (k) and all the remaining ones, but grouping them in those classes with a rank lower than k, and those with a rank higher than k. Therefore, it can be considered as a reformulation of the well-known one-versus-all scheme. The base algorithm for the ensemble could be any threshold (or even probabilistic) method, such as the ones selected in this paper: kernel discriminant analysis, support vector machines and logistic regression (all reformulated to deal with ordinal regression problems). The method is seen to be competitive when compared with other state-of-the-art methodologies (both ordinal and nominal), by using six measures and a total of fifteen ordinal datasets. Furthermore, an additional set of experiments is used to study the potential scalability and interpretability of the proposed method when using logistic regression as base methodology for the ensemble. ", awards = "JCR(2014): 3.469 Position: 2/24 (Q1) Category: COMPUTER SCIENCE, CYBERNETICS", comments = "JCR(2014): 3.469 Position: 2/24 (Q1) Category: COMPUTER SCIENCE, CYBERNETICS", doi = "10.1109/TCYB.2013.2266336", issn = "2168-2267", journal = "IEEE Transactions on Cybernetics", keywords = "Ordinal regression, ordinal classification, en- semble, threshold models, discriminant analysis, support vector machines, logistic regression, ordinal decomposition", month = "May", note = "JCR(2014): 3.469 Position: 2/24 (Q1) Category: COMPUTER SCIENCE, CYBERNETICS", number = "5", pages = "681--694 ", title = "{P}rojection based ensemble learning for ordinal regression", url = "http://www.uco.es/grupos/ayrna/elor2013", volume = "44", year = "2014", }

Nota:

JCR(2014): 3.469 Position: 2/24 (Q1) Category: COMPUTER SCIENCE, CYBERNETICS

Abstract:

The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper proposes an ensemble methodology specifically adapted to this type of problems, which is based on computing different classification tasks through the formulation of different order hypotheses. Every single model is trained in order to distinguish between one given class (k) and all the remaining ones, but grouping them in those classes with a rank lower than k, and those with a rank higher than k. Therefore, it can be considered as a reformulation of the well-known one-versus-all scheme. The base algorithm for the ensemble could be any threshold (or even probabilistic) method, such as the ones selected in this paper: kernel discriminant analysis, support vector machines and logistic regression (all reformulated to deal with ordinal regression problems). The method is seen to be competitive when compared with other state-of-the-art methodologies (both ordinal and nominal), by using six measures and a total of fifteen ordinal datasets. Furthermore, an additional set of experiments is used to study the potential scalability and interpretability of the proposed method when using logistic regression as base methodology for the ensemble.

Comentarios:

JCR(2014): 3.469 Position: 2/24 (Q1) Category: COMPUTER SCIENCE, CYBERNETICS