[1] Ding Weiping, , Wang Jiandong, et al. Co-evolutionary cloud-based attribute ensemblemulti-agent reduction algorithm [J]. Journal of Southeast University (English Edition), 2016, 32 (4): 432-438. [doi:10.3969/j.issn.1003-7985.2016.04.007]

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Co-evolutionary cloud-based attribute ensemblemulti-agent reduction algorithm()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

32

Issue:

2016 4

Page:

432-438

Research Field:

Computer Science and Engineering

Publishing date:

2016-12-20

Info

Title:

Co-evolutionary cloud-based attribute ensemblemulti-agent reduction algorithm

Author(s):

Ding Weiping¹;  2;  4;  Wang Jiandong³;  Zhang Xiaofeng²;  Guan Zhijin²

¹State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
²School of Computer Science and Technology, Nantong University, Nantong 226019, China
³College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
⁴Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing 210094, China

Keywords:

co-evolutionary elitist optimization;  attribute reduction;  co-evolutionary cloud framework;  multi-agent ensemble strategy;  neonatal brain 3D-MRI

PACS:

TP301

DOI:

10.3969/j.issn.1003-7985.2016.04.007

Abstract:

In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction(CCAEMR)algorithm is proposed. First, a co-evolutionary cloud framework is designed under the MapReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation. Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance. Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.

References:

[1] Pawlak Z. Rough sets [J]. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356. DOI:10.1007/bf01001956.
[2] Hassanien A. Fuzzy rough sets hybrid scheme for breast cancer detection [J]. Image and Vision Computing, 2007, 25(2): 172-183. DOI:10.1016/j.imavis.2006.01.026.
[3] Maji P, Garai P. IT2 fuzzy-rough sets and max relevance-max significance criterion for attribute selection [J]. IEEE Transactions on Cybernetics, 2015, 45(8): 1657-1668. DOI:10.1109/TCYB.2014.2357892.
[4] Zhao S Y, Chen H, Li C P, et al. RFRR: Robust fuzzy rough reduction [J]. IEEE Transactions on Fuzzy Systems, 2013, 21(5): 825-841. DOI:10.1109/tfuzz.2012.2231417.
[5] Zhao S Y, Chen H, Li C P, et al. A novel approach to building a robust fuzzy rough classifier [J]. IEEE Transactions on Fuzzy Systems, 2015, 23(4): 769-786. DOI:10.1109/tfuzz.2014.2327993.
[6] Yao Y, Zhao Y. Discernibility matrix simplification for constructing attribute reducts [J]. Information Sciences, 2009, 179(7): 867-882. DOI:10.1016/j.ins.2008.11.020.
[7] Wu X, Zhu X, Wu G Q, et al. Data mining with big data [J]. IEEE Transactions on Knowledge and Data Engineer, 2014, 26(1): 97-107. DOI: 10.1109/TKDE.2013.109.
[8] Zhang J B, Li T R, Ruan D, et al. A parallel method for computing rough set approximations [J]. Information Sciences, 2012, 194: 209-223. DOI:10.1016/j.ins.2011.12.036.
[9] Zhang J B, Wong J S, Li T R, et al. A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems [J]. International Journal of Approximate Reasoning, 2014, 55(3): 896-907. DOI:10.1016/j.ijar.2013.08.003.
[10] Qian J, Miao D Q, Zhang Z H, et al. Parallel attribute reduction algorithms using MapReduce[J]. Information Sciences, 2014, 279: 671-690. DOI:10.1016/j.ins.2014.04.019.
[11] Xu F F, Lei J S, Bi Z Q, et al. Approaches to approximate reduction with interval-valued multi-decision tables in big data[J]. Journal of Software, 2014, 25(9): 2119-2135 DOI:10.13328/j.cnki.jos.004640.(in Chinese)
[12] You S, Baldick R. Hybrid coevolutionary programming for Nash equilibrium search in games with local optima [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(4): 305-315. DOI:10.1109/tevc.2004.832862.
[13] Jiang T Z. Brainnetome: A new-ome to understand the brain and its disorders [J]. Neuroimage, 2013, 80: 263-272. DOI:10.1016/j.neuroimage.2013.04.002.
[14] Knickmeyer R C, Gouttard S, Kang C, et al. A structural MRI study of human brain development from birth to 2 years[J]. Journal of Neuroscience, 2008, 28(47): 12176-12182. DOI:10.1523/JNEUROSCI.3479-08.2008.
[15] Warfield S K, Zou K H, Wells W M. Simultaneous truth and performance level estimation(STAPLE): An algorithm for the validation of image segmentation [J]. IEEE Transactions on Medical Imaging, 2004, 23(7): 903-921. DOI:10.1109/TMI.2004.828354.
[16] Wang L, Shi F, Li G, et al. Segmentation of neonatal brain MR images using patch-driven level sets [J]. Neuroimage, 2014, 84: 141-158. DOI:10.1016/j.neuroimage.2013.08.008.

Memo

Memo:

Biography: Ding Weiping(1979—), male, doctor, associate professor, dwp9988@163.com.
Foundation items: The National Natural Science Foundation of China(No.61300167), the Open Project Program of State Key Laboratory for Novel Software Technology of Nanjing University(No.KFKT2015B17), the Natural Science Foundation of Jiangsu Province(No.BK20151274), Qing Lan Project of Jiangsu Province, the Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education(No.JYB201606), the Program for Special Talent in Six Fields of Jiangsu Province(No.XYDXXJS-048).
Citation: Ding Weiping, Wang Jiandong, Zhang Xiaofeng, et al. Co-evolutionary cloud-based attribute ensemble multi-agent reduction algorithm[J].Journal of Southeast University(English Edition), 2016, 32(4):432-438.DOI:10.3969/j.issn.1003-7985.2016.04.007.

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