4.7 Article

A unifying view on dataset shift in classification

期刊

PATTERN RECOGNITION
卷 45, 期 1, 页码 521-530

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.06.019

关键词

Dataset shift; Data fracture; Changing environments; Differing training and test populations; Covariate shift; Sample selection bias; Non-stationary distributions

资金

  1. Ministerio de Educacion y Ciencia of the Spanish Government
  2. Spanish Government [TIN2008-06681-006-01]
  3. National Science Foundation (NSF) [ECCS0926170]
  4. [DPI2009-08424]
  5. [TEC2008-01348/TEC]
  6. Directorate For Engineering [0926170] Funding Source: National Science Foundation

向作者/读者索取更多资源

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. (C) 2011 Elsevier Ltd. All rights reserved.

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