E. Ikonomovska, J. Gama, and S. D?eroski, Learning model trees from evolving data streams, Data Mining and Knowledge Discovery, vol.23, issue.1, pp.128-168, 2011.
DOI : 10.1007/s10618-010-0201-y

E. Almeida, C. Ferreira, and J. Gama, Adaptive Model Rules from Data Streams, Proceedings of ECML PKDD, pp.480-492, 2013.
DOI : 10.1007/978-3-642-40988-2_31
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.638.5472

J. Duarte and J. Gama, Ensembles of Adaptive Model Rules from High-Speed Data Streams, Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining, pp.198-213, 2014.

A. L. Strehl and M. L. Littman, Online linear regression and its application to model-based reinforcement learning, Advances in Neural Information Processing Systems, pp.737-744, 2007.

Y. Wiener and R. El-yaniv, Pointwise Tracking the Optimal Regression Function, Neural Information Processing Systems, pp.2051-2059, 2012.

B. Kégl, Robust regression by boosting the median. Learning Theory and Kernel Machines, pp.258-272, 2003.

A. Gammerman and V. Vovk, Hedging Predictions in Machine Learning: The Second Computer Journal Lecture, The Computer Journal, vol.50, issue.2, pp.151-163, 2007.
DOI : 10.1093/comjnl/bxl065

M. Toplak, Assessment of Machine Learning Reliability Methods for Quantifying the Applicability Domain of QSAR Regression Models, Journal of Chemical Information and Modeling, vol.54, issue.2, pp.431-441, 2014.
DOI : 10.1021/ci4006595

Z. Bosni´cbosni´c and I. Kononenko, Comparison of approaches for estimating reliability of individual regression predictions, Data & Knowledge Engineering, vol.67, issue.3, pp.504-516, 2008.
DOI : 10.1016/j.datak.2008.08.001

P. P. Rodrigues, J. Gama, and Z. Bosni´cbosni´c, Online Reliability Estimates for Individual Predictions in Data Streams, 2008 IEEE International Conference on Data Mining Workshops, pp.36-45, 2008.
DOI : 10.1109/ICDMW.2008.123

A. Shaker and E. Hüllermeier, IBLStreams: a system for instance-based classification and regression on data streams, Evolving Systems, vol.28, issue.12, pp.235-249, 2012.
DOI : 10.1007/s12530-012-9059-0

T. R. Hoens, N. V. Chawla, and R. Polikar, Heuristic Updatable Weighted Random Subspaces for Non-stationary Environments, 2011 IEEE 11th International Conference on Data Mining, pp.241-250, 2011.
DOI : 10.1109/ICDM.2011.75
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.363.5876

. Webb, Characterizing concept drift, Data Mining and Knowledge Discovery, vol.23, issue.1, pp.1-31, 2015.
DOI : 10.1145/1401890.1401987
URL : http://arxiv.org/abs/1511.03816

V. N. Balasubramanian, S. Ho, V. Vovk, H. Wechsler, F. S. Li et al., Conformal Prediction for Reliable Machine Learning No Longer Confidential: Estimating the Confidence of Individual Regression Predictions, PLoS ONE, vol.7, issue.11, 2012.

Z. Bosni´cbosni´c and I. Kononenko, Automatic selection of reliability estimates for individual regression predictions, The Knowledge Engineering Review, vol.24, issue.01, pp.27-47, 2010.
DOI : 10.1023/A:1010933404324

E. Ikonomovska, Algorithms for Learning Regression Trees and Ensembles on Evolving Data Streams, 2012.

A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer, MOA: Massive Online Analysis, Journal of Machine Learning Research, vol.11, pp.1601-1604, 2010.
DOI : 10.1007/978-3-642-41398-8_9

J. H. Friedman, Multivariate Adaptive Regression Splines, The Annals of Statistics, vol.19, issue.1, pp.1-67, 1991.
DOI : 10.1214/aos/1176347963
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.382.970

E. S. Page, CONTINUOUS INSPECTION SCHEMES, Biometrika, vol.41, issue.1-2, pp.100-115, 1954.
DOI : 10.1093/biomet/41.1-2.100

G. Krempl, Open challenges for data stream mining research, SIGKDD Explorations, p.2014
DOI : 10.1145/2674026.2674028
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.458.7333

L. Devroye, L. Gyorfi, and G. Lugosi, A probabilistic theory of pattern recognition, 1996.
DOI : 10.1007/978-1-4612-0711-5

A. Khosravi, S. Nahavandi, D. Creighton, and A. F. Atiya, Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances, IEEE Transactions on Neural Networks, vol.22, issue.9, pp.1341-1356, 2011.
DOI : 10.1109/TNN.2011.2162110