| 基于人工鱼群算法的孪生支持向量机 |
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| 引用本文： | 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. |
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| 作者姓名： | 李景灿 丁世飞 |
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| 作者单位： | 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 |
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| 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。
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| 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 |
Twin support vector machine based on artificial fish swarm algorithm |
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| LI Jingcan,,DING Shifei,.Twin support vector machine based on artificial fish swarm algorithm[J].CAAL Transactions on Intelligent Systems,2019,14(6):1121-1126. |
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| Authors: | LI Jingcan DING Shifei |
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| Affiliation: | 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 |
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| 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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| Keywords: | twin support vector machine artificial fish swarm algorithm pattern classification parameter optimization accuracy swarm intelligence quadratic programming parallel processing global optimization |
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