Title Ordinal Regression Methods: Survey and Experimental Study
Authors Gutiérrez P.A. , PÉREZ ORTIZ, MARÍA, SÁNCHEZ MONEDERO, JAVIER, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Hervás-Martínez C.
External publication No
Means IEEE Trans Knowl Data Eng
Scope Conference Paper
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 3.438
SJR Impact 1.325
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961578448&doi=10.1109%2fTKDE.2015.2457911&partnerID=40&md5=9ae3f35e7552a9836564afdb82da6dce
Publication date 01/01/2016
ISI 000366833100011
Scopus Id 2-s2.0-84961578448
DOI 10.1109/TKDE.2015.2457911
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. © 2015 IEEE.
Keywords Artificial intelligence; Bins; Classification (of information); Learning systems; Neural networks; Support vector machines; Taxonomies; Augmented binaries; Binary decompositions; discriminant learning
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