A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 2 Issue 3
Jul.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Li Liu, Aolei Yang, Wenju Zhou, Xiaofeng Zhang, Minrui Fei and Xiaowei Tu, "Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 3, pp. 235-247, 2015.
Citation: Li Liu, Aolei Yang, Wenju Zhou, Xiaofeng Zhang, Minrui Fei and Xiaowei Tu, "Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 3, pp. 235-247, 2015.

Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means


This work was supported by National Natural Science Foundation of China (61403244, 61304031), Key Project of Science and Technology Commission of Shanghai Municipality (14JC1402200), the Shanghai Municipal Commission of Economy and Informatization under Shanghai Industry-University- Research Collaboration (CXY-2013-71), the Science and Technology Commission of Shanghai Municipality under 'Yangfan Program' (14YF1408600), National Key Scientific Instrument and Equipment Development Project (2012YQ15008703), and Innovation Program of Shanghai Municipal Education Commission (14YZ007).

  • Dataset classification is an essential fundament of computational intelligence in cyber-physical systems (CPS). Due to the complexity of CPS dataset classification and the uncertainty of clustering number, this paper focuses on clarifying the dynamic behavior of acceleration dataset which is achieved from micro electro mechanical systems (MEMS) and complex image segmentation. To reduce the impact of parameters uncertainties with dataset classification, a novel robust dataset classification approach is proposed based on neighbor searching and kernel fuzzy c-means (NSKFCM) methods. Some optimized strategies, including neighbor searching, controlling clustering shape and adaptive distance kernel function, are employed to solve the issues of number of clusters, the stability and consistency of classification, respectively. Numerical experiments finally demonstrate the feasibility and robustness of the proposed method.


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