Publication
Title
Analyzing concept drift and shift from sample data
Author
Abstract
Concept drift and shift are major issues that greatly affect the accuracy and reliability of many real-world applications of machine learning. We propose a new data mining task, concept drift mapping-the description and analysis of instances of concept drift or shift. We argue that concept drift mapping is an essential prerequisite for tackling concept drift and shift. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift and shift in marginal distributions. We present quantitative concept drift mapping techniques, along with methods for visualizing their results. We illustrate their effectiveness for real-world applications across energy-pricing, vegetation monitoring and airline scheduling.
Language
English
Source (journal)
Data mining and knowledge discovery. - Boston, Mass.
Source (book)
European Conference on Machine Learning and Principles and Practice of, Knowledge Discovery in Databases (ECML PKDD), SEP 10-14, 2018, Dublin, IRELAND
Publication
Dordrecht : Springer, 2018
ISSN
1384-5810
Volume/pages
32:5(2018), p. 1179-1199
ISI
000441940300002
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 08.10.2018
Last edited 15.09.2021
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