Ordinal Regression Methods: Survey and Experimental Study
Ordinal Regression Methods: Survey and Experimental Study
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DOI：
10.1109/tkde.2015.2457911
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发表时间：
2016-01-01
影响因子：
8.9
通讯作者：
Hervas-Martinez, Cesar
中科院分区：
文献类型：
--
作者：
Antonio Gutierrez, Pedro;Perez-Ortiz, Maria;Hervas-Martinez, Cesar
有序回归问题是那些机器学习问题，其目标是使用分类尺度对模式进行分类，该分类尺度显示标签之间的自然顺序。许多现实世界的应用程序提出了这种标记结构，并增加了过去几年在该领域开发的方法和算法的数量。虽然有序回归可以使用标准的名义分类技术，但有几种算法可以特别受益于排序信息。因此，本文的目的是回顾这些技术的最新发展，并提出了一个分类的基础上，如何构建模型，以考虑到顺序。此外，提出了一个彻底的实验研究，以检查是否使用的顺序信息，提高了所获得的模型的性能，考虑到一些方法内的分类。结果证实，排序信息的好处，提高其准确性和接近的预测实际目标的有序规模的有序模型。
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.