Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9950
Title: Scalable TSK fuzzy modeling for very large datasets using minimal-enclosing-ball approximation
Authors: Deng, Z
Choi, KS 
Chung, FL 
Wang, S
Keywords: ε-insensitive training
Core set
core vector machine (CVM)
minimal-enclosing-ball (MEB) approximation
TakagiSugenoKang (TSK) fuzzy modeling
very large datasets
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on fuzzy systems, 2011, v. 19, no. 2, 5629439, p. 210-226 How to cite?
Journal: IEEE transactions on fuzzy systems 
Abstract: In order to overcome the difficulty in TakagiSugenoKang (TSK) fuzzy modeling for large datasets, scalable TSK (STSK) fuzzy-model training is investigated in this study based on the core-set-based minimal-enclosing-ball (MEB) approximation technique. The specified L2-norm penalty-based ε-insensitive criterion is first proposed for TSK-model training, and it is found that such TSK fuzzy-model training can be equivalently expressed as a center-constrained MEB problem. With this finding, an STSK fuzzy-model-training algorithm, which is called STSK, for large or very large datasets is then proposed by using the core-set-based MEB-approximation technique. The proposed algorithm has two distinctive advantages over classical TSK fuzzy-model training algorithms: The maximum space complexity for training is not reliant on the size of the training dataset, and the maximum time complexity for training is linear with the size of the training dataset, as confirmed by extensive experiments on both synthetic and real-world regression datasets.
URI: http://hdl.handle.net/10397/9950
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2010.2091961
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