Systems, Man & Cybernetics - April 2016 - 32

[11] M. I. Jordan, "Divide-and-conquer and statistical inference for big data," presented

Divide-andConquer
Sampling

Incremental
Learning
Going from
Big to Small

Granular
Computing

Feature
Selection

Remove
Redundancy

at the 14th Computing in the 21st Century Conf., Tianjin, China, Oct. 2012.
[12] H. B. He, S. Chen, K. Li, and X. Xu, "Incremental learning from stream data,"
IEEE Trans. Neural Netw. Learn. Syst., vol. 22, no. 1, pp. 1901-1919, 2011.
[13] S. L. Lohr, Sampling: Design and Analysis, 2nd ed. Boston, MA: Cengage
Learning, 2009.
[14] C. L. P. Chen and C. Y. Zhang, "Data-intensive applications, challenges, techniques
and technologies: A survey on big data," Inform. Sci., vol. 275, pp. 314-347, Aug. 2014.
[15] W. Pedrycz, Granular Computing: Analysis and Design of Intelligent

Figure 4. the theme for big data analytics is to

change big into small. the uncertainty model and
processing play a key part for these methodologies
of going from big to small.

Systems. Boca Raton, FL: CRC Press/Francis Taylor, 2013.
[16] Z. X. Zhu, S. Jia, and Z. Ji, "Towards a memetic feature selection paradigm,"
IEEE Comput. Intell. Mag., vol. 5, no. 2, pp. 41-53, 2010.
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About the Authors
Xizhao Wang (xzwang@szu.edu.cn) earned his Ph.D.
degree in computer science from the Harbin Institute of Technology, China, in 1998. He is currently a professor in the College of Computer Science and Software Engineering at Shenzhen University, China. His research interests include learning from examples with fuzzy representation, fuzzy measures
and integrals, neuro-fuzzy systems and genetic algorithms,
feature extraction, multiclassifier fusion, and the recent learning from big data. He is a Distinguished Lecturer of the IEEE
Systems, Man, and Cybernetics Society and an IEEE Fellow.
Yulin He (yulinhe@szu.edu.cn) earned his M.S. degree
in computer science and his Ph.D. degree in optical engineering from Hebei University in 2009 and 2014, respectively. He is currently a postdoctoral fellow in the College
of Computer Science and Software Engineering at Shenzhen University, China. His research interests include the
Bayesian network, artificial neural networks, evolutionary
optimization, probability density estimation, and approximate reasoning. He is an IEEE Member.

[18] X. Z. Wang, R. A. R. Ashfaq, and A. M. Fu, "Fuzziness based sample categorization
for classifier performance improvement," J. Intelligent Fuzzy Syst., vol. 29, no. 3,
pp. 1185-1196, 2015.
[19] X. Z. Wang, H. J. Xing, Y. Li, Q. Hua, C. R. Dong, and W. Pedrycz, "A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble
learning," IEEE Trans. Fuzzy Syst., vol. 23, no. 5, pp. 1638-1654, 2015.
[20] X. Z. Wang, R. Wang, H. M. Feng, and H. C. Wang, "A new approach to classifier fusion
based on upper integral," IEEE Trans. Cybern., vol. 44, no. 5, pp. 620-635, 2014.
[21] X. Z. Wang, Y. L. He, and D. D. Wang, "Non-naive Bayesian classifiers for classification problems with continuous attributes," IEEE Trans. Cybern., vol. 44, no. 1,
pp. 21-39, 2014.
[22] X. Z. Wang, L. C. Dong, and J. H. Yan, "Maximum ambiguity based sample selection in fuzzy decision tree induction," IEEE Trans. Knowl. Data Eng., vol. 24, no.
8, pp. 1491-1505, 2012.
[23] X. Z. Wang and C. R. Dong, "Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy," IEEE Trans. Fuzzy Syst., vol. 17, no. 3, pp. 556-567, 2009.
[24] Z. B. Xu, J. Y. Liang, C. Y. Dang, and K. S. Chin, "Inclusion degree: A perspective on
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IEEE SyStEmS, man, & CybErnEtICS magazInE A pri l 2016


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Table of Contents for the Digital Edition of Systems, Man & Cybernetics - April 2016

Systems, Man & Cybernetics - April 2016 - Cover1
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