|Table of Contents|

[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]
Copy

Co-evolutionary cloud-based attribute ensemblemulti-agent reduction algorithm()
基于协同进化云的属性集成多代理约简算法
Share:

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
基于协同进化云的属性集成多代理约简算法
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
丁卫平1 2 4 王建东3 张晓峰2 管致锦2
1南京大学计算机软件新技术国家重点实验室, 南京 210093; 2南通大学计算机科学与技术学院, 南通 226019; 3南京航空航天大学计算机科学与技术学院, 南京 210016; 4南京理工大学高维信息智能感知与系统教育部重点实验室, 南京 210094
Keywords:
co-evolutionary elitist optimization attribute reduction co-evolutionary cloud framework multi-agent ensemble strategy neonatal brain 3D-MRI
协同进化精英优化 属性约简 协同云框架 集成多代理策略 婴幼儿脑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.
为提高属性约简算法处理含噪音和不确定大数据的性能, 提出了一种基于协同进化云的属性集成多代理约简算法(CCAEMR).该算法首先基于MapReduce机制设计协同进化云框架, 将整个种群分解成多个具有自适应规模的协同进化子种群, 通过子种群的共享奖酬来加速属性约简实现.然后, 构造了一种协同精英优化的多代理集成策略, 确保划分的子种群能够充分探索交叠属性子集之间的相关性和相互依赖性, 且具有较强的抗噪音性能, 这些代理能保持在稳定的精英地区且取得了最佳收益.实验结果表明:所提出的CCAEMR算法在解决大规模和不确定复杂噪音数据的属性约简时具有更好的效率和适用性.

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.
Last Update: 2016-12-20