Drift mining in data - A framework for addressing drift in classification.

Vera Hofer; Georg Krempl

doi:10.1016/J.CSDA.2012.07.007

Drift mining in data - A framework for addressing drift in classification.

Sub Algorithmic Data Analysis

Research output: Contribution to journal › Article › Academic › peer-review

Original language	English
Pages (from-to)	377-391
Journal	Computational Statistics and Data Analysis
Volume	57
Issue number	1
DOIs	https://doi.org/10.1016/J.CSDA.2012.07.007
Publication status	Published - 2013

Bibliographical note

DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Access to Document

10.1016/J.CSDA.2012.07.007

https://dblp.org/rec/journals/csda/HoferK13

Cite this

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title = "Drift mining in data - A framework for addressing drift in classification.",

author = "Vera Hofer and Georg Krempl",

note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

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AU - Krempl, Georg

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PY - 2013

Y1 - 2013

U2 - 10.1016/J.CSDA.2012.07.007

DO - 10.1016/J.CSDA.2012.07.007

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JO - Computational Statistics and Data Analysis

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