Output details
11 - Computer Science and Informatics
Lancaster University
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Output 0 of 0 in the submission
Article title
Evolving Fuzzy Rule-based Classifiers from Data Streams
Type
D - Journal article
Title of journal
IEEE Transactions on Fuzzy Systems
Article number
-
Volume number
16
Issue number
6
First page of article
1462
ISSN of journal
1063-6706
Year of publication
2008
Number of additional authors
1
Additional information
<12> This paper proposes a novel approach to the classification of data in online streams in which classifier rules are derived dynamically and automatically. The technique, and associated software, is highly significant in facilitating real-time data stream analysis in a “Big Data” context, and has had direct industrial impact, including on Ford (who have licenced the technique from Lancaster - $14K) and Sagem. The paper is widely cited and was nominated for an Outstanding IEEE Transaction paper award in 2010. IEEE Transactions on Fuzzy Systems is a top-ranked journal (latest IF 5.4).
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
85
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
