[1]李景灿,丁世飞.基于人工鱼群算法的孪生支持向量机[J].智能系统学报,2019,14(06):1121-1126.[doi:10.11992/tis.201905025]
 LI Jingcan,DING Shifei.Twin support vector machine based on artificial fish swarm algorithm[J].CAAI Transactions on Intelligent Systems,2019,14(06):1121-1126.[doi:10.11992/tis.201905025]
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基于人工鱼群算法的孪生支持向量机(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年06期
页码:
1121-1126
栏目:
出版日期:
2019-11-05

文章信息/Info

Title:
Twin support vector machine based on artificial fish swarm algorithm
作者:
李景灿12 丁世飞12
1. 中国矿业大学 计算机科学与技术学院 江苏 徐州 221116;
2. 矿山数字化教育部工程研究中心, 江苏 徐州 221116
Author(s):
LI Jingcan12 DING Shifei12
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
2. Mine Digitization Engineering Research Center of Minstry of Education of the People’s Republic of China, Xuzhou 221116, China
关键词:
孪生支持向量机人工鱼群算法模式分类参数优化准确率群体智能二次规划并行处理全局优化
Keywords:
twin support vector machineartificial fish swarm algorithmpattern classificationparameter optimizationaccuracyswarm intelligencequadratic programmingparallel processingglobal optimization
分类号:
TP391.4
DOI:
10.11992/tis.201905025
摘要:
孪生支持向量机(twin support vector machine, TWSVM)是在支持向量机的基础上产生的机器学习算法,具有训练速度快、分类性能优越等优点。但是孪生支持向量机无法很好地处理参数选择问题,不合适的参数会降低分类能力。人工鱼群算法(artificial fish swarm algorithm, AFSA)是一种群智能优化算法,具有较强的全局寻优能力和并行处理能力。本文将孪生支持向量机与人工鱼群算法结合,来解决孪生支持向量机的参数选择问题。首先将孪生支持向量机的参数作为人工鱼的位置信息,同时将分类准确率作为目标函数,然后通过人工鱼的觅食、聚群、追尾和随机行为来更新位置和最优解,最后迭代结束时得到最优参数和最优分类准确率。该算法在训练过程中自动确定孪生支持向量机的参数,避免了参数选择的盲目性,提高了孪生支持向量机的分类性能。
Abstract:
Twin support vector machine (TWSVM) is a machine learning algorithm based on the support vector machine. It has the advantages of fast training speed and superior classification performance. However, the algorithm cannot handle the parameter selection problem effectively, and the inappropriate parameters will reduce the classification ability. The artificial fish swarm algorithm (AFSA) is a group intelligent optimization algorithm with a strong global optimization ability and parallel processing capability. In this paper, TWSVM and AFSA are combined to solve the parameter selection problem of the TWSVM. First, the parameters of the support vector machine are taken as the position information of the artificial fish, and the classification accuracy is taken as the objective function. Then, the position and the optimal solution are updated by the artificial fish’s preying, swarming, following, and random behavior. At the end of the iterations, the optimal parameters and the optimal classification accuracy are obtained. The algorithm automatically determines the parameters of the TWSVM in the training process, avoiding the blindness of parameter selection, and improves the classification performance of the TWSVM

参考文献/References:

[1] 丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1):1-10 DING Shifei, QI Bingjuan, TAN Hongyan. An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technol-ogy of China, 2011, 40(1):1-10
[2] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3):273-297.
[3] CHEN Zhensong, QI Zhiquan, WANG Bo, et al. Learning with label proportions based on nonparallel support vector machines[J]. Knowledge-based systems, 2017, 119:126-141.
[4] CHEN Zhenyu, FAN Zhiping. Distributed customer be-havior prediction using multiplex data:a collaborative MK-SVM approach[J]. Knowledge-based systems, 2012, 35:111-119.
[5] MORAES R, VALIATI J F, NETO W P G. Docu-ment-level sentiment classification:an empirical compar-ison between SVM and ANN[J]. Expert systems with ap-plications, 2013, 40(2):621-633.
[6] ZHANG Xiekai, DING Shifei, XUE Yu. An improved multiple birth support vector machine for pattern classi-fication[J]. Neurocomputing, 2017, 225:119-128.
[7] DING Shifei, ZHANG Xiekai, AN Yuexuan, et al. Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification[J]. Pattern recognition, 2017, 67:32-46.
[8] SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural processing letters, 1999, 9(3):293-300.
[9] FUNG G, MANGASARIAN O L. Proximal support vec-tor machine classifiers[C]//Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Dis-covery and Data Mining. San Francisco, California, 2001:77-86.
[10] MANGASARIAN O L, WILD E W. Multisurface proxi-mal support vector machine classification via generalized eigenvalues[J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28(1):69-74.
[11] JAYADEVA, KHEMCHANDANI R, CHANDRA S. Twin support vector machines for pattern classification[J]. IEEE transactions on pattern analysis and machine intel-ligence, 2007, 29(5):905-910.
[12] HUANG Huajuan, WEI Xiuxi, ZHOU Yongquan. Twin support vector machines:a survey[J]. Neurocomputing, 2018, 300:34-43.
[13] 丁世飞. 孪生支持向量机:理论、算法与拓展[M]. 北京:科学出版社, 2017:16-31. DING Shifei. Twin support vector machine:theory, algo-rithm and extension[M]. Beijing:Science Press, 2017:16-31.
[14] KUMAR M A, GOPAL M. Application of smoothing technique on twin support vector machines[J]. Pattern recognition letters, 2008, 29(13):1842-1848.
[15] KUMAR M A, GOPAL M. Least squares twin support vector machines for pattern classification[J]. Expert sys-tems with applications, 2009, 36(4):7535-7543.
[16] KUMAR M A, KHEMCHANDANI R, Gopal M, et al. Knowledge based Least Squares Twin support vector machines[J]. Information sciences, 2010, 180(23):4606-4618.
[17] MIR A, NASIRI J A. KNN-based least squares twin sup-port vector machine for pattern classification[J]. Applied intelligence, 2018, 48(12):4551-4564.
[18] CHEN Sugen, WU Xiaojun. A new fuzzy twin support vector machine for pattern classification[J]. International journal of machine learning and cybernetics, 2018, 9(9):1553-1564.
[19] CHEN Xiaobo, YANG Jian, YE Qiaolin, et al. Recursive projection twin support vector machine via within-class variance minimization[J]. Pattern recognition, 2011, 44(10/11):2643-2655.
[20] XIE Xiaomin. Improvement on projection twin support vector machine[J]. Neural computing and applications, 2018, 30(2):371-387.
[21] XU Zongben, DAI Mingwei, MENG Deyu. Fast and effi-cient strategies for model selection of Gaussian support vector machine[J]. IEEE transactions on systems, man, and cybernetics, part B (cybernetics), 2009, 39(5):1292-1307.
[22] DING Shifei, YU Junzhao, HUANG Huajuan, et al. Twin support vector machines based on particle swarm optimi-zation[J]. Journal of computers, 2013, 8(9):2296-2303.
[23] DING Shifei, WU Fulin, NIE Ru, et al. Twin support vector machines based on quantum particle swarm opti-mization[J]. Journal of software, 2013, 8(7):1743-1750.
[24] ZHAI Shijun, PAN Juan, LUO Hongwei, et al. A new sense-through-foliage target recognition method based on hybrid particle swarm optimization-based wavelet twin support vector machine[J]. Measurement, 2016, 80:58-70.
[25] SARTAKHTI J S, AFRABANDPEY H, SARAEE M. Simulated annealing least squares twin support vector machine (SA-LSTSVM) for pattern classification[J]. Soft computing, 2017, 21(15):4361-4373.
[26] 李晓磊, 邵之江, 钱积新. 一种基于动物自治体的寻优模式:鱼群算法[J]. 系统工程理论与实践, 2002, 22(11):32-38 LI Xiaolei, SHAO Zhijiang, QIAN Jixin. An optimizing method based on autonomous animats:fish-swarm algo-rithm[J]. Systems engineering-theory & practice, 2002, 22(11):32-38

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备注/Memo

备注/Memo:
收稿日期:2019-05-13。
基金项目:国家自然科学基金项目(61672522,61379101).
作者简介:李景灿,男,1995年生,硕士研究生,主要研究方向为支持向量机和机器学习;丁世飞,男,1963年生,教授,博士生导师,主要研究方向为人工智能、机器学习、模式识别、数据挖掘。主持国家重点基础研究计划(973计划)课题1项、国家自然科学基金面上项目2项。出版专著4部,发表学术论文200余篇。
通讯作者:丁世飞.E-mail:dingsf@cumt.edu.cn
更新日期/Last Update: 2019-12-25