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Wrapper-based fuzzy inductive reasoning model identification for imbalance data classification

Author
Bagherpour, S.; Nebot, A.; Mugica, F.
Type of activity
Presentation of work at congresses
Name of edition
2018 IEEE International Conference on Fuzzy Systems
Date of publication
0
Presentation's date
2018-07-10
Book of congress proceedings
2018 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2018: Rio de Janeiro, Brazil, July 8-13, 2018
First page
1
Last page
8
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/FUZZ-IEEE.2018.8491622 Open in new window
Repository
http://hdl.handle.net/2117/124561 Open in new window
URL
https://ieeexplore.ieee.org/document/8491622 Open in new window
Abstract
Fuzzy Inductive Reasoning (FIR) is a qualitative inductive modeling and simulation methodology for dealing with complex dynamical systems. FIR has proven to be a powerful tool for qualitative model identification and prediction of future be-havior of different kinds of system domains including biology, medicine, ecology, etc. FIR has been mainly applied to regression problems, but recently we are interested in studying the feasibility of FIR as a classifier. The main objective of this study is t...
Citation
Bagherpour, S., Nebot, M., Múgica, F. Wrapper-based fuzzy inductive reasoning model identification for imbalance data classification. A: IEEE World Congress on Computational Intelligence. "2018 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2018: Rio de Janeiro, Brazil, July 8-13, 2018". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1-8.
Keywords
Classification, Fuzzy inductive reasoning, Imbalance data, Wraper-based models
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants

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