Multi-view TSK Fuzzy System via Collaborative Learning

CHENG Yang; GU Xiaoqing; JIANG Yizhang; HANG Wenlong; QIAN Pengjiang; WANG Shitong

doi:10.11999/JEIT151209

Volume 38 Issue 8

Sep. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(8): 2054-2061

CHENG Yang, GU Xiaoqing, JIANG Yizhang, HANG Wenlong, QIAN Pengjiang, WANG Shitong. Multi-view TSK Fuzzy System via Collaborative Learning[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2054-2061. doi: 10.11999/JEIT151209

Citation:

CHENG Yang, GU Xiaoqing, JIANG Yizhang, HANG Wenlong, QIAN Pengjiang, WANG Shitong. Multi-view TSK Fuzzy System via Collaborative Learning[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2054-2061. doi: 10.11999/JEIT151209

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Multi-view TSK Fuzzy System via Collaborative Learning

doi: 10.11999/JEIT151209

Funds:

The National Natural Science Foundation of China (61300151), The Natural Science Foundation of Jiangsu Province (BK20130155), The RD Frontier Grant of Jiangsu Province (BY2013015-02), The Fundamental Research Funds for the Central Universities (JUSRP51614A)

Received Date: 2015-10-29
Rev Recd Date: 2016-03-15
Publish Date: 2016-08-19

Abstract

Abstract

Conventional fuzzy system modeling methods essentially belong to the single-view learning modality. In multi-view-oriented data scenarios, they can only cope with each view separately, which is prone to incurring their unsatisfactory generalization performance. In response to such problem, the fuzzy system modeling method with the ability of multi-view learning is pursued. To this end, based on the classic L2 norm Takagi-Sugeno-Kang (TSK) fuzzy system, by means of the collaborative learning items qualified for multi-view learning, the core Multi-View TSK Fuzzy System (MV-TSK-FS) modeling method is presented. MV-TSK-FS can not only effectively utilize the independent components composed of the characteristics affiliated to each view, but also take full advantage of the potential information occurred by the interrelated effects among views, which eventually facilitates its relatively strong generalization ability. The experimental results performed on both synthetic and real-life datasets indicate that, compared with some traditional single-view methods, this propounded multi-view fuzzy modeling system owns preferable applicability as well as generalization.
- Multi-view learning,
- Collaborative learning,
- Fuzzy modeling,
- Takagi-Sugeno-Kang (TSK) type fuzzy system

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References(17)

References

LI Guangxia, CHANG Kuiyu, and HOI S C H. Multiview semi-supervised learning with consensus[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(11): 2040-2051. doi: 10.1109/TKDE.2011.160.

HONG Chaoqun, YU Jun, YOU Jane, et al. Multi-view ensemble manifold regularization for 3D object recognition [J]. Information Sciences, 2015, 320: 395-405. doi: 10.1016/ j.ins.2015.03.032.

ZHANG Shunli, YU Xin, SUI Yao, et al. Object tracking with multi-view support vector machines[J]. IEEE Transactions on Multimedia, 2015, 17(3): 265-278. doi: 10.1109/TMM. 2015.2390044.

蒋亦樟, 邓赵红, 王骏, 等. 熵加权多视角协同划分模糊聚类算法[J]. 软件学报, 2014, 25(10): 2293-2311. doi: 10.13328/j. cnki.jos.004510.

JIANG Yizhang, DENG Zhaohong, WANG Jun, et al. Collaborative partition multi-view fuzzy clustering algorithm using entropy weighting[J]. Journal of Software, 2014, 25(10): 2293-2311. doi: 10.13328/j.cnki.jos.004510.

TZORTZIS G F and LIKAS A C. Multiple view clustering using a weighted combination of exemplar-based mixture models[J]. IEEE Transactions on Neural Network, 2010, 21(12): 1925-1938. doi: 10.1109/TNN.2010.2081999.

JIANG Yizhang, CHUNG Fulai, WANG Shitong, et al. Collaborative fuzzy clustering from multiple weighted views[J]. IEEE Transactions on Cybernetics, 2015, 45(4): 688-701. doi: 10.1109/TCYB.2014.2334595.

MERUGU S, ROSSET S, and PERLICH C. A new multi- view regression approach with an application to customer wallet estimation[C]. Proceedings of 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, 2006: 656-661.

DENG Zhaohong, JIANG Yizhang, CHOI Kupsze, et al. Knowledge-leverage-based TSK fuzzy system modeling[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(8): 1200-1212. doi: 10.1109/FUZZ-IEEE. 2014.6891544.

STRM K J and MCAVOY T J. Intelligent control[J]. Journal of Process Control, 1992, 2(3): 115-127.

邓赵红, 张江滨, 蒋亦樟, 等. 基于模糊子空间聚类的〇阶L2型TSK模糊系统[J]. 电子与信息学报, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074.

DENG Zhaohong, ZHANG Jiangbin, JIANG Yizhang, et al. Fuzzy subspace clustering based zero-order L2-norm TSK fuzzy system[J]. Journal of Electronics Information Technology, 2015, 37(9): 2082-2088.

TAKAGI T and SUGENO M. Fuzzy identification of systems and its application to modeling and control[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(1): 116-132. doi: 10.1109/TSMC.1985.6313399.

MAMDANI E H. Application of fuzzy logic to approximate reasoning using linguistic synthesis[J]. IEEE Transactions on Computers, 1977, 26(12): 1182-1191. doi: 10.1109/TC.1977. 1674779.

AZEEM M F, HANMANDLU M, and AHMAD N. Generalization of adaptive neural-fuzzy inference systems[J]. IEEE Transactions on Neural Networks, 2000, 11(6): 1332-1346. doi: 10.1109/72.883438.

JIANG Yizhang, CHUNG Fulai, and WANG Shitong. Enhanced fuzzy partitions vs data randomness in FCM[J]. Journal of Intelligent and Fuzzy Systems, 2014, 27(4): 1639-1648. doi: 10.3233/IFS-141130.

ITO K and NAKANO R. Optimizing support vector regression hyperparameters based on cross-validation[C]. Proceedings of the International Joint Conference on Neural Networks, Jantzen Beach, Portland, Oregon, 2003: 2077-2082.

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