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Erschienen in: International Journal of Machine Learning and Cybernetics 1/2012

01.03.2012 | Original Article

Ordinal regression with continuous pairwise preferences

verfasst von: Maria Dobrska, Hui Wang, William Blackburn

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2012

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Abstract

We investigate the problem of data-driven ordinal regression—the problem of learning to rank order new data items based on information inherent in existing data items. Ordinal regression shares common features with multi-category classification and metric regression. However, it requires new, tailor-made methodologies to reduce prediction error. The approach has application in various domains, including information retrieval, collaborative filtering and social sciences. We propose a new distribution-independent methodology for ordinal regression based on pairwise preferences employing information about strength of dependency between two data instances, which we refer to as continuous preferences. Our hypothesis is that additional information about strength of preference as well as its direction can improve algorithmic performance. We also provide a novel technique for deriving ordinal regression labels from pairwise information. Experimental results on real-world ordinal and metric regression data sets confirm usefulness of the methodology compared with other state-of-the-art approaches.

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Fußnoten
1
Weka configuration for SMOreg: weka.classifiers.functions.SMOreg -C 1.0 -N 0 -I “weka.classifiers.functions.supportVector.RegSMOImproved -L 0.0010 -W 1 -P 1.0E-12 -T 0.0010 -V” -K “weka.classifiers.functions.supportVector.RBFKernel -C 250007 -G 0.01” -t.
 
3
Authors’ descriptions from the original data sets.
 
Literatur
1.
Zurück zum Zitat Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: ICML’05: Proceedings of the 22nd international conference on machine learning. ACM, New York, NY, USA, pp 89–96 Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: ICML’05: Proceedings of the 22nd international conference on machine learning. ACM, New York, NY, USA, pp 89–96
3.
Zurück zum Zitat Cohen WW, Schapire RE, Singer Y (1998) Learning to order things. In: NIPS’97: Proceedings of the 1997 conference on advances in neural information processing systems 10. MIT Press, Cambridge, MA, USA, pp 451–457 Cohen WW, Schapire RE, Singer Y (1998) Learning to order things. In: NIPS’97: Proceedings of the 1997 conference on advances in neural information processing systems 10. MIT Press, Cambridge, MA, USA, pp 451–457
4.
Zurück zum Zitat Crammer K, Singer Y (2001) Pranking with ranking. In: NIPS, pp 641–647 Crammer K, Singer Y (2001) Pranking with ranking. In: NIPS, pp 641–647
5.
Zurück zum Zitat Frank E, Hall M (2001) A simple approach to ordinal classification. In: EMCL’01: Proceedings of the 12th European conference on machine learning. Springer, London, UK, pp 145–156 Frank E, Hall M (2001) A simple approach to ordinal classification. In: EMCL’01: Proceedings of the 12th European conference on machine learning. Springer, London, UK, pp 145–156
6.
Zurück zum Zitat Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969MathSciNet Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969MathSciNet
7.
Zurück zum Zitat Herbrich R, Graepel T, Obermayer K (1999) Support vector learning for ordinal regression. In: In international conference on artificial neural networks, pp 97–102 Herbrich R, Graepel T, Obermayer K (1999) Support vector learning for ordinal regression. In: In international conference on artificial neural networks, pp 97–102
9.
Zurück zum Zitat Huhn J, Hullermeier E (2009) Is an ordinal class structure useful in classifier learning?. Int J Data Min Model Manag 1(1):45–67CrossRef Huhn J, Hullermeier E (2009) Is an ordinal class structure useful in classifier learning?. Int J Data Min Model Manag 1(1):45–67CrossRef
11.
Zurück zum Zitat Rennie JD, Srebro N (2005) Loss functions for preference levels: regression with discrete ordered labels. In: Proceedings of the IJCAI multidisciplinary workshop on advances in preference handling, pp 180–186 Rennie JD, Srebro N (2005) Loss functions for preference levels: regression with discrete ordered labels. In: Proceedings of the IJCAI multidisciplinary workshop on advances in preference handling, pp 180–186
12.
Zurück zum Zitat Shashua A, Levin A (2002) Ranking with large margin principle: two approaches. In: NIPS, pp 937–944 Shashua A, Levin A (2002) Ranking with large margin principle: two approaches. In: NIPS, pp 937–944
13.
Zurück zum Zitat Shevade SK, Chu W (2006) Minimum enclosing spheres formulations for support vector ordinal regression. In: ICDM’06: Proceedings of the sixth international conference on data mining. IEEE Computer Society, Washington, DC, USA, pp 1054–1058 Shevade SK, Chu W (2006) Minimum enclosing spheres formulations for support vector ordinal regression. In: ICDM’06: Proceedings of the sixth international conference on data mining. IEEE Computer Society, Washington, DC, USA, pp 1054–1058
14.
Zurück zum Zitat Torra V, Domingo-Ferrer J, Mateo-Sanz JM, Ng M (2006) Regression for ordinal variables without underlying continuous variables. Inf Sci 176(4):465–474MathSciNetCrossRef Torra V, Domingo-Ferrer J, Mateo-Sanz JM, Ng M (2006) Regression for ordinal variables without underlying continuous variables. Inf Sci 176(4):465–474MathSciNetCrossRef
15.
Zurück zum Zitat Waegeman W, Baets BD, Boullart L (2008) Roc analysis in ordinal regression learning. Pattern Recogn Lett 29(1):1–9CrossRef Waegeman W, Baets BD, Boullart L (2008) Roc analysis in ordinal regression learning. Pattern Recogn Lett 29(1):1–9CrossRef
16.
Zurück zum Zitat Witten IH, Frank E (2005) Data Mining: practical machine learning tools and techniques. Morgan Kaufmann, San Fransisco Witten IH, Frank E (2005) Data Mining: practical machine learning tools and techniques. Morgan Kaufmann, San Fransisco
17.
Zurück zum Zitat Zhao B, Wang F, Zhang C (2009) Block-quantized support vector ordinal regression. IEEE Trans Neural Netw 20(5):882–90CrossRef Zhao B, Wang F, Zhang C (2009) Block-quantized support vector ordinal regression. IEEE Trans Neural Netw 20(5):882–90CrossRef
Metadaten
Titel
Ordinal regression with continuous pairwise preferences
verfasst von
Maria Dobrska
Hui Wang
William Blackburn
Publikationsdatum
01.03.2012
Verlag
Springer-Verlag
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2012
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0036-x

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