Signed-Error Conformal Regression

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)


This paper suggets a modification of the Conformal Prediction framework for regression that will strenghten the associated guarantee of validity. We motivate the need for this modification and argue that our conformal regressors are more closely tied to the actual error distribution of the underlying model, thus allowing for more natural interpretations of the prediction intervals. In the experimentation, we provide an empirical comparison of our conformal regressors to traditional conformal regressors and show that the proposed modification results in more robust two-tailed predictions, and more efficient one-tailed predictions.


Conformal Prediction prediction intervals regression 


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  1. 1.
    Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world. Springer Verlag, DE (2006)Google Scholar
  2. 2.
    Gammerman, A., Vovk, V., Vapnik, V.: Learning by transduction. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 148–155. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  3. 3.
    Saunders, C., Gammerman, A., Vovk, V.: Transduction with confidence and credibility. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI 1999), vol. 2, pp. 722–726 (1999)Google Scholar
  4. 4.
    Shafer, G., Vovk, V.: A tutorial on conformal prediction. The Journal of Machine Learning Research 9, 371–421 (2008)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Papadopoulos, H., Proedrou, K., Vovk, V., Gammerman, A.: Inductive confidence machines for regression. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 345–356. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Papadopoulos, H., Vovk, V., Gammerman, A.: Regression conformal prediction with nearest neighbours. Journal of Artificial Intelligence Research 40(1), 815–840 (2011)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Papadopoulos, H.: Inductive conformal prediction: Theory and application to neural networks. Tools in Artificial Intelligence 18, 315–330, 2 (2008)Google Scholar
  8. 8.
    Papadopoulos, H., Haralambous, H.: Reliable prediction intervals with regression neural networks. Neural Networks 24(8), 842–851 (2011)CrossRefGoogle Scholar
  9. 9.
    Lambrou, A., Papadopoulos, H., Gammerman, A.: Reliable confidence measures for medical diagnosis with evolutionary algorithms. IEEE Transactions on Information Technology in Biomedicine 15(1), 93–99 (2011)CrossRefGoogle Scholar
  10. 10.
    Papadopoulos, H.: Inductive conformal prediction: Theory and application to neural networks. Tools in Artificial Intelligence 18, 315–330 (2008)Google Scholar
  11. 11.
    Papadopoulos, H., Papatheocharous, E., Andreou, A.S.: Reliable confidence intervals for software effort estimation. In: AIAI Workshops, pp. 211–220. Citeseer (2009)Google Scholar
  12. 12.
    Devetyarov, D., Nouretdinov, I., Burford, B., Camuzeaux, S., Gentry-Maharaj, A., Tiss, A., Smith, C., Luo, Z., Chervonenkis, A., Hallett, R., et al.: Conformal predictors in early diagnostics of ovarian and breast cancers. Progress in Artificial Intelligence 1(3), 245–257 (2012)CrossRefGoogle Scholar
  13. 13.
    Papadopoulos, H., Gammerman, A., Vovk, V.: Reliable diagnosis of acute abdominal pain with conformal prediction. Engineering Intelligent Systems 17(2), 127 (2009)Google Scholar
  14. 14.
    Lambrou, A., Papadopoulos, H., Kyriacou, E., Pattichis, C.S., Pattichis, M.S., Gammerman, A., Nicolaides, A.: Assessment of stroke risk based on morphological ultrasound image analysis with conformal prediction. In: Papadopoulos, H., Andreou, A.S., Bramer, M. (eds.) AIAI 2010. IFIP AICT, vol. 339, pp. 146–153. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Bache, K., Lichman, M.: UCI machine learning repository (2013),
  16. 16.
    Rasmussen, C.E., Neal, R.M., Hinton, G., van Camp, D., Revow, M., Ghahramani, Z., Kustra, R., Tibshirani, R.: Delve data for evaluating learning in valid experiments (1996),

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Business and InformaticsUniversity of BoråsBoråsSweden

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