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== Reference ==
 
== Reference ==
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Willem Waegeman, Bernard De Baets: [[A Survey on ROC-based Ordinal Regression]]. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 127-154.  
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Willem Waegeman, Bernard De Baets: [[A Survey on ROC-based Ordinal Regression]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 127-154.  
  
 
== DOI ==
 
== DOI ==
Zeile 27: Zeile 27:
  
 
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    J.Xu, Y.Cao, H.Li, Y.Huang, Cost-sensitive learning of SVM for ranking, in Proceedings of the 17th European Conference on Machine Learning(Berlin, Germany, 2006), pp. 833–840
 
 
      
 
      
−
65.
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65. L.Yan, R.Dodier, M.Mozer, R.Wolniewiecz, Optimizing classifier performance via an approximation to the Wilcoxon-Mann-Whitney statistic, in Proceedings of the International Conference on Machine Learning(Washington, DC, USA, 2003), pp. 848–855
−
    L.Yan, R.Dodier, M.Mozer, R.Wolniewiecz, Optimizing classifier performance via an approximation to the Wilcoxon-Mann-Whitney statistic, in Proceedings of the International Conference on Machine Learning(Washington, DC, USA, 2003), pp. 848–855
 
 
      
 
      
−
66.
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66. S.Yu, K.Yu, V.Tresp, H.Kriegel, Collaborative ordinal regression, in Proceedings of the International Conference on Machine Learning(Pittsburgh, PA, 2006), pp. 1089–1096
−
    S.Yu, K.Yu, V.Tresp, H.Kriegel, Collaborative ordinal regression, in Proceedings of the International Conference on Machine Learning(Pittsburgh, PA, 2006), pp. 1089–1096
 
  
  

Aktuelle Version vom 30. November 2015, 13:05 Uhr

Reference

Willem Waegeman, Bernard De Baets: A Survey on ROC-based Ordinal Regression. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 127-154.

DOI

http://dx.doi.org/10.1007/978-3-642-14125-6_7

Abstract

Ordinal regression can be seen as a special case of preference learning, in which the class labels corresponding with data instances can take values from an ordered finite set. In such a setting, the classes usually have a linguistic interpretation attached by humans to subdivide the data into a number of preference bins. In this chapter, we give a general survey on ordinal regression from a machine learning point of view. In particular, we elaborate on some important connections with ROC analysis that have been introduced recently by the present authors. First, the important role of an underlying ranking function in ordinal regression models is discussed, as well as its impact on the performance evaluation of such models. Subsequently, we describe a new ROC-based performance measure that directly evaluates the underlying ranking function, and we place it in the more general context of ROC analysis as the volume under an r-dimensional ROC surface (VUS) for in general rclasses. Furthermore, we also discuss the scalability of this measure and show that it can be computed very efficiently for large samples. Finally, we present a kernel-based learning algorithm that optimizes VUS as a specific case of structured support vector machines.

Extended Abstract

Bibtex

@incollection{
year={2011},
isbn={978-3-642-14124-9},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6_7},
title={A Survey on ROC-based Ordinal Regression},
url={http://dx.doi.org/10.1007/978-3-642-14125-6_7, http://de.evo-art.org/index.php?title=A_Survey_on_ROC-based_Ordinal_Regression },
publisher={Springer Berlin Heidelberg},
author={Waegeman, Willem and Baets, BernardDe},
pages={127-154},
language={English}
}

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Links

Full Text

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Sonstige Links

W. Waegeman. Learning to Rank: a ROC-based Graph-Theoretic Approach. PhD Thesis, Ghent University, October 2008 http://users.ugent.be/~wwaegemn/thesis.pdf

W. Waegeman: Learning to Rank: a ROC-based Graph-Theoretic Approach. 4OR quarterly journal of the Belgian, French and Italian Operations Research Societies 7(4): 399-402 · November 2009 http://link.springer.com/article/10.1007%2Fs10288-009-0095-y Abstract of PhD Thesis

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