Very Fast Decision Rules for Classification in Data Streams
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00081934" target="_blank" >RIV/00216224:14330/15:00081934 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s10618-013-0340-z" target="_blank" >http://dx.doi.org/10.1007/s10618-013-0340-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10618-013-0340-z" target="_blank" >10.1007/s10618-013-0340-z</a>
Alternative languages
Result language
angličtina
Original language name
Very Fast Decision Rules for Classification in Data Streams
Original language description
Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. Many decision tasks can be formulated as stream mining problems and therefore many new algorithms for data streams are being proposed. Decision rules are one of the most interpretable and flexible models for predictive data mining. Nevertheless, few algorithms have been proposed in the literature to learn rule models for time-changing and high-speed flows of data. In this paper we present the very fast decision rules (VFDR) algorithm and discuss interesting extensions to the base version. All the proposed versions are one-pass and any-time algorithms. They work on-line and learn ordered or unordered rule sets. Algorithms designed to work with datastreams should be able to detect changes and quickly adapt the decision model. In order to manage these situations we also present the adaptive extension (AVFDR) to detect changes in the process generating data and adapt the decision mode
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
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
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Data Mining and Knowledge Discovery
ISSN
1384-5810
e-ISSN
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Volume of the periodical
29
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
35
Pages from-to
168-202
UT code for WoS article
000347948900006
EID of the result in the Scopus database
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