Ordinal regression methods: survey and experimental study

Áreas de investigación:

• Methodology - Machine learning

Año:

2016

Tipo de publicación:

Artículo

Palabras clave:

ordinal regression, ordinal classification, binary decomposition, threshold methods, augmented binary classification, proportional odds model, support vector machines, discriminant learning, artificial neural networks

Autores:

• Gutiérrez, Pedro Antonio
• Pérez-Ortiz, María
• Sánchez-Monedero, Javier
• Fernandez-Navarro, Francisco
• Hervás-Martínez, César

Journal:

IEEE Transactions on Knowledge and Data Engineering

Volumen:

28

Número:

1

Páginas:

127-146

Mes:

January

ISSN:

1041-4347

BibTex:

@article{ORReview2016, author = "Pedro Antonio Guti{\'e}rrez and Mar{\'i}a P{\'e}rez-Ortiz and Javier S{\'a}nchez-Monedero and Francisco Fernandez-Navarro and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and that has increased the number of methods and algorithms developed over the last years in this field. Although ordinal regression can be faced using standard nominal classification techniques, there are several algorithms which can specifically benefit from the ordering information. Therefore, this paper is aimed at reviewing the state of the art on these techniques and proposing a taxonomy based on how the models are constructed to take the order into account. Furthermore, a thorough experimental study is proposed to check if the use of the order information improves the performance of the models obtained, considering some of the approaches within the taxonomy. The results confirm that ordering information benefits ordinal models improving their accuracy and the closeness of the predictions to actual targets in the ordinal scale. ", awards = "JCR(2016): 3.438 Position: 21/146 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", comments = "JCR(2016): 3.438 Position: 21/146 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", doi = "10.1109/TKDE.2015.2457911", issn = "1041-4347", journal = "IEEE Transactions on Knowledge and Data Engineering", keywords = "ordinal regression, ordinal classification, binary decomposition, threshold methods, augmented binary classification, proportional odds model, support vector machines, discriminant learning, artificial neural networks", month = "January", note = "JCR(2016): 3.438 Position: 21/146 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", number = "1", pages = "127-146", title = "{O}rdinal regression methods: survey and experimental study", url = "http://dx.doi.org/10.1109/TKDE.2015.2457911", volume = "28", year = "2016", }

Nota:

JCR(2016): 3.438 Position: 21/146 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS

Abstract:

Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and that has increased the number of methods and algorithms developed over the last years in this field. Although ordinal regression can be faced using standard nominal classification techniques, there are several algorithms which can specifically benefit from the ordering information. Therefore, this paper is aimed at reviewing the state of the art on these techniques and proposing a taxonomy based on how the models are constructed to take the order into account. Furthermore, a thorough experimental study is proposed to check if the use of the order information improves the performance of the models obtained, considering some of the approaches within the taxonomy. The results confirm that ordering information benefits ordinal models improving their accuracy and the closeness of the predictions to actual targets in the ordinal scale.

Comentarios:

JCR(2016): 3.438 Position: 21/146 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS