Ordinal regression methods: survey and experimental study

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Author
Gutiérrez, Pedro A.
Pérez-Ortiz, María
Sánchez-Monedero, J.
Fernández-Navarro, Francisco
Hervás-Martínez, César
Date
2017-02-03Subject
Ordinal regressionOrdinal classification
Binary decomposition
Threshold methods
Discriminant learning
Artificial neural networks
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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