Handling Time Changing Data with Adaptive Very Fast Decision Rules
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F12%3A00061019" target="_blank" >RIV/00216224:14330/12:00061019 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-33460-3_58" target="_blank" >http://dx.doi.org/10.1007/978-3-642-33460-3_58</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-33460-3_58" target="_blank" >10.1007/978-3-642-33460-3_58</a>
Alternative languages
Result language
angličtina
Original language name
Handling Time Changing Data with Adaptive Very Fast Decision Rules
Original language description
Data streams are usually characterized by changes in the underlying distribution generating data. Therefore algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. Rules are one of the most interpretable and flexible models for data mining prediction tasks. In this paper we present the Adaptive Very Fast Decision Rules (AVFDR), an on-line, any-time and one-pass algorithm for learning decision rules in the context of time changing data. AVFDR can learn ordered and unordered rule sets. It is able to adapt the decision model via incremental induction and specialization of rules. Detecting local drifts takes advantage of the modularity of rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules that detect drift.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/LA09016" target="_blank" >LA09016: Czech Republic membership in the European Research Consortium for Informatics and Mathematics (ERCIM)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases ECML/PKDD
ISBN
9783642334597
ISSN
0302-9743
e-ISSN
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Number of pages
16
Pages from-to
827-842
Publisher name
Springer Berlin / Heidelberg
Place of publication
Berlin / Heidelberg
Event location
Bristol
Event date
Jan 1, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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