2015/07/17 by Pedro Antonio Gutiérrez, María Pérez‐Ortiz, Javier Sánchez‐Monedero +2 · 1 citation
Computer Science · #Neural Networks and Applications #Imbalanced Data Classification Techniques #Machine Learning and Data Classification
paper · doi:10.1109/tkde.2015.2457911
openalex publication_date 2015/07/17 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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.