Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors: Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Takafumi Kanamori, Klaus-Robert Mller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schlkopf, Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama, Choon Hui Teo Neural Information Processing series
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- Long M, Zhu H, Wang J and Jordan M Deep transfer learning with joint adaptation networks Proceedings of the 34th International Conference on Machine Learning - Volume 70, (2208-2217)
- Sechidis K, Sperrin M, Petherick E, Lujn M and Brown G (2017). Dealing with under-reported variables, International Journal of Approximate Reasoning, 85:C, (159-177), Online publication date: 1-Jun-2017.
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- Minkov E (2015). Event Extraction using Structured Learning and Rich Domain Knowledge, ACM Transactions on Intelligent Systems and Technology, 7:2, (1-34), Online publication date: 22-Jan-2016.
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- Raykar V and Saha A Data split strategies for evolving predictive models Proceedings of the 2015th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I, (3-19)
- Wen J, Greiner R and Schuurmans D Correcting covariate shift with the Frank-Wolfe algorithm Proceedings of the 24th International Conference on Artificial Intelligence, (1010-1016)
- Esuli A and Sebastiani F (2015). Optimizing Text Quantifiers for Multivariate Loss Functions, ACM Transactions on Knowledge Discovery from Data, 9:4, (1-27), Online publication date: 1-Jun-2015.
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- Khosla A, Zhou T, Malisiewicz T, Efros A and Torralba A Undoing the damage of dataset bias Proceedings of the 12th European conference on Computer Vision - Volume Part I, (158-171)
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- Cai P, Gao W, Zhou A and Wong K Relevant knowledge helps in choosing right teacher Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, (115-124)
- Fernández A, García S and Herrera F Addressing the classification with imbalanced data Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I, (1-10)
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- Cornuéjols A On-line learning Ubiquitous knowledge discovery, (129-147)
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- Tadipatri V, Gowreesunker B, Tewfik A, Ince N, Ashe J and Pellizzer G Spatial proximity based subspace decomposition for movement direction decoding of local field potentials Proceedings of the 43rd Asilomar conference on Signals, systems and computers, (1090-1093)
- Fernandez A, Galar M, Sanz J, Bustince H, Cordon O and Herrera F On the impact of Distance-based Relative Competence Weighting approach in One-vs-One classification for Evolutionary Fuzzy Systems: DRCW-FH-GBML algorithm 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (1-7)
Index Terms
- Dataset Shift in Machine Learning
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