Ordinal Regression Methods: Survey and Experimental Study

By Pedro Antonio Gutiérrez, María Pérez-Ortiz, Javier Sánchez-Monedero, Francisco Fernández-Navarro, C. Hervás‐Martínez. IEEE Transactions on Knowledge and Data Engineering, 2016

445 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d67edf85746a2fc64f803de44cc4169b87525c27

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Citations in the corpus

Big data preprocessing: methods and prospects (2016, background): “This is the case of ordinal classification/regression [111], multi-label classification [52, 53, 112] or multi-instance learning [54].”