[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(6):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(6):1121-1126.[doi:10.11992/tis.201905025]
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LI Jingcan,DING Shifei.Twin support vector machine based on artificial fish swarm algorithm[J].CAAI Transactions on Intelligent Systems,2019,14(6):1121-1126.[doi:10.11992/tis.201905025]
基于人工鱼群算法的孪生支持向量机
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第6期
页码:
1121-1126
栏目:
学术论文—机器学习
出版日期:
2019-11-05
- Title:
- Twin support vector machine based on artificial fish swarm algorithm
- Keywords:
- twin support vector machine; artificial fish swarm algorithm; pattern classification; parameter optimization; accuracy; swarm intelligence; quadratic programming; parallel processing; global optimization
- 分类号:
- TP391.4
- 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
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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