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

Li Liu; Aolei Yang; Wenju Zhou; Xiaofeng Zhang; Minrui Fei; Xiaowei Tu

Volume 2 Issue 3

Jul. 2015

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2015 > 2(3): 235-247

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.

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

1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China;
2. School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK;
3. School of Information Science and Electrical Engineering, Ludong University, Yantai 264025, China

Funds:

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).

Abstract

Abstract

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.
- Dataset classification,
- neighbor searching,
- variable weight,
- kernel fuzzy c-means,
- robustness estimation

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References

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

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