Abstract
The field of dataset shift has received a growing amount of interest in the last few years. The fact that most real-world applications have to cope with some form of shift makes its study highly relevant. The literature on the topic is mostly scattered, and different authors use different names to refer to the same concepts, or use the same name for different concepts. With this work, we attempt to present a unifying framework through the review and comparison of some of the most important works in the literature. ► Presentation of a unifying framework for the field of dataset shift, focusing on classification. ► Analysis of the terminology used in the most relevant works of the field. ► Formal definitions for each of the concepts appearing in the study of dataset shift, including sample selection bias.
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