G. Bansal, A. Sinha, and H. Zhao. 2008. Tuning data mining methods for cost-sensitive regression: A study in loan charge-off forecasting. Journal of Management Information System 25, 3 (Dec. 2008), 315--336.

A. P. Basu and N. Ebrahimi. 1992. Bayesian approach to life testing and reliability estimation using asymmetric loss function. Journal of Statistical Planning and Inference 29, 1--2 (1992), 21--31.

A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2010. Quantification via probability estimators. In Proceedings of the 2010 IEEE International Conference on Data Mining. IEEE, 737--742.
[+]

G. Bansal, A. Sinha, and H. Zhao. 2008. Tuning data mining methods for cost-sensitive regression: A study in loan charge-off forecasting. Journal of Management Information System 25, 3 (Dec. 2008), 315--336.

A. P. Basu and N. Ebrahimi. 1992. Bayesian approach to life testing and reliability estimation using asymmetric loss function. Journal of Statistical Planning and Inference 29, 1--2 (1992), 21--31.

A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2010. Quantification via probability estimators. In Proceedings of the 2010 IEEE International Conference on Data Mining. IEEE, 737--742.

A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2013. Aggregative quantification for regression. Data Mining and Knowledge Discovery (2013), 1--44.

A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2009. Calibration of machine learning models. In Handbook of Research on Machine Learning Applications. IGI Global, 128--146.

A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2011. Using negotiable features for prescription problems. Computing 91, 2 (2011), 135--168.

J. Bi and K. P. Bennett. 2003. Regression error characteristic curves. In Proceedings of the 20th International Conference on Machine Learning (ICML’03).

Z. Bosnić and I. Kononenko. 2008. Comparison of approaches for estimating reliability of individual regression predictions. Data & Knowledge Engineering 67, 3 (2008), 504--516.

Z. Bosnić and I. Kononenko. 2009. An overview of advances in reliability estimation of individual predictions in machine learning. Intelligent Data Analysis 13, 2 (2009), 385--401.

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth.

P. F. Christoffersen and F. X. Diebold. 1996. Further results on forecasting and model selection under asymmetric loss. Journal of Applied Econometrics 11, 5 (1996), 561--571.

P. F. Christoffersen and F. X. Diebold. 1997. Optimal prediction under asymmetric loss. Econometric Theory 13 (1997), 808--817.

I. Cohen and M. Goldszmidt. 2004. Properties and benefits of calibrated classifiers. Knowledge Discovery in Databases: PKDD 2004 (2004), 125--136.

S. Crone. 2002. Training artificial neural networks for time series prediction using asymmetric cost functions. In Proceedings of the 9th International Conference on Neural Information Processing.

J. Demšar. 2006. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7 (2006), 1--30.

M. Dumas, L. Aldred, G. Governatori, and A. H. M. Ter Hofstede. 2005. Probabilistic automated bidding in multiple auctions. Electronic Commerce Research 5, 1 (2005), 25--49.

C. Elkan. 2001. The foundations of cost-sensitive learning. In Proceedings of the 17th International Conference on Artificial Intelligence (’01), Bernhard Nebel (Ed.). San Francisco, CA, 973--978.

G. Elliott and A. Timmermann. 2004. Optimal forecast combinations under general loss functions and forecast error distributions. Journal of Econometrics 122, 1 (2004), 47--79.

T. Fawcett. 2006a. An introduction to ROC analysis. Pattern Recognition Letters 27, 8 (2006), 861--874.

T. Fawcett. 2006b. ROC graphs with instance-varying costs. Pattern Recognition Letters 27, 8 (2006), 882--891.

C. Ferri, P. Flach, and J. Hernández-Orallo. 2002. Learning decision trees using the area under the ROC curve. In Proceedings of the International Conference on Machine Learning. 139--146.

C. Ferri, P. Flach, and J. Hernández-Orallo. 2003. Improving the AUC of probabilistic estimation trees. In Proceedings of the 14th European Conference on Machine Learning (ECML’03). Springer, 121--132.

C. Ferri and J. Hernández-Orallo. 2004. Cautious classifiers. In ROC Analysis in Artificial Intelligence, 1st International Workshop, ROCAI-2004, Valencia, Spain, August 22, 2004, J. Hernández-Orallo, C. Ferri, N. Lachiche, and P. A. Flach (Eds.). 27--36.

P. Flach. 2012. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press.

G. Forman. 2008. Quantifying counts and costs via classification. Data Mining and Knowledge Discovery 17, 2 (2008), 164--206.

S. García and F. Herrera. 2008. An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. The Journal of Machine Learning Research 9, 2677--2694 (2008), 66.

R. Ghani. 2005. Price prediction and insurance for online auctions. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD’05). ACM, New York, NY, 411--418.

C. W. J. Granger. 1969. Prediction with a generalized cost of error function. Operational Research (1969), 199--207.

C. W. J. Granger. 1999. Outline of forecast theory using generalized cost functions. Spanish Economic Review 1, 2 (1999), 161--173.

P. Hall, J. Racine, and Q. Li. 2004. Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association 99, 468 (2004), 1015--1026.

P. Hall, R. C. L. Wolff, and Q. Yao. 1999. Methods for estimating a conditional distribution function. Journal of the American Statistical Association (1999), 154--163.

T. J. Hastie, R. J. Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.

J. Hernández-Orallo. 2013. ROC curves for regression. Pattern Recognition 46, 12 (2013), 3395--3411.

J. Hernández-Orallo, P. Flach, and C. Ferri. 2012. A unified view of performance metrics: Translating threshold choice into expected classification loss. Journal of Machine Learning Research 13 (2012), 2813--2869.

J. Hernández-Orallo, P. Flach, and C. Ferri. 2013. ROC curves in cost space. Machine Learning 93, 1 (2013), 71--91.

J. N. Hwang, S. R. Lay, and A. Lippman. 1994. Nonparametric multivariate density estimation: A comparative study. IEEE Transactions on Signal Processing 42, 10 (1994), 2795--2810.

R. J. Hyndman, D. M. Bashtannyk, and G. K. Grunwald. 1996. Estimating and visualizing conditional densities. Journal of Computational and Graphical Statistics (1996), 315--336.

N. Japkowicz and M. Shah. 2011. Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press.

M. Jino, B. T. de Abreu, and others. 2010. Machine learning methods and asymmetric cost function to estimate execution effort of software testing. In Proceedings of the 2010 3rd International Conference on Software Testing, Verification and Validation (ICST’10). IEEE, 275--284.

B. Kitts and B. Leblanc. 2004. Optimal bidding on keyword auctions. Electronic Markets 14, 3 (2004), 186--201.

N. Lachiche and P. Flach. 2003. Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In Proceedings of the International Conference on Machine Learning, Vol. 20-1. 416.

H. Papadopoulos. 2008. Inductive conformal prediction: Theory and application to neural networks. Tools in Artificial Intelligence 18 (2008), 315--330.

H. Papadopoulos, K. Proedrou, V. Vovk, and A. Gammerman. 2002. Inductive confidence machines for regression. In Machine Learning: ECML 2002, Tapio Elomaa, Heikki Mannila, and Hannu Toivonen (Eds.). Lecture Notes in Computer Science, Vol. 2430. Springer, Berlin, 185--194.

H. Papadopoulos, V. Vovk, and A. Gammerman. 2011. Regression conformal prediction with nearest neighbours. Journal of Artificial Intelligence Research 40, 1 (2011), 815--840.

T. Pietraszek. 2007. On the use of ROC analysis for the optimization of abstaining classifiers. Machine Learning 68, 2 (2007), 137--169.

J. C. Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, Boston, 61--74.

F. Provost and P. Domingos. 2003. Tree induction for probability-based ranking. Machine Learning 52, 3 (2003), 199--215.

R Team and others. 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

R. Ribeiro. 2011. Utility-based Regression. PhD thesis, Department of Computer Science, Faculty of Sciences, University of Porto.

M. Rosenblatt. 1969. Conditional probability density and regression estimators. Multivariate Analysis II 25 (1969), 31.

S. Rosset, C. Perlich, and B. Zadrozny. 2007. Ranking-based evaluation of regression models. Knowledge and Information Systems 12, 3 (2007), 331--353.

R. E. Schapire, P. Stone, D. McAllester, M. L. Littman, and J. A. Csirik. 2002. Modeling auction price uncertainty using boosting-based conditional density estimation. In Proceedings of the International Conference on Machine Learning. 546--553.

G. Shafer and V. Vovk. 2008. A tutorial on conformal prediction. Journal of Machine Learning Research 9 (2008), 371--421.

J. A. Swets, R. M. Dawes, and J. Monahan. 2000. Better decisions through science. Scientific American 283, 4 (Oct. 2000), 82--87.

R. D. Thompson and A. P. Basu. 1996. Asymmetric loss functions for estimating system reliability. In Bayesian Analysis in Statistics and Econometrics. John Wiley & Sons, 471--482.

L. Torgo. 2005. Regression error characteristic surfaces. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 697--702.

L. Torgo. 2010. Data Mining with R. Chapman and Hall/CRC Press.

L. Torgo and R. Ribeiro. 2007. Utility-based regression. Knowledge Discovery in Databases: PKDD 2007. 597--604.

L. Torgo and R. Ribeiro. 2009. Precision and recall for regression. In Discovery Science. Springer, 332--346.

P. Turney. 2000. Types of cost in inductive concept learning. Canada National Research Council Publications Archive.

L. Wasserman. 2006. All of Nonparametric Statistics. Springer-Verlag, New York.

M. P. Wellman, D. M. Reeves, K. M. Lochner, and Y. Vorobeychik. 2004. Price prediction in a trading agent competition. Journal of Artificial Intelligence Research 21 (2004), 19--36.

K. Yu and M. C. Jones. 2004. Likelihood-based local linear estimation of the conditional variance function. Journal of the American Statistical Association 99, 465 (2004), 139--144.

B. Zadrozny and C. Elkan. 2002. Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 694--699.

A. Zellner. 1986. Bayesian estimation and prediction using asymmetric loss functions. Journal of the American Statistical Association (1986), 446--451.

H. Zhao, A. P. Sinha, and G. Bansal. 2011. An extended tuning method for cost-sensitive regression and forecasting. Decision Support Systems.

[-]