Support vector regression based on optimal training subset and adaptive particle swarm optimization algorithm
Support vector regression based on optimal training subset and adaptive particle swarm optimization algorithm
复制标题
基于最优训练子集和自适应粒子群优化算法的支持向量回归
DOI：
10.1016/j.asoc.2013.04.003
复制
发表时间：
2013-08-01
影响因子：
8.7
通讯作者：
Che, JinXing
中科院分区：
文献类型：
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作者：
Che, JinXing
支持向量回归（SVR）由于其在小样本、非线性和高维数据应用中具有吸引力的特征和深刻的经验表现，在机器学习领域变得非常有前景和流行。然而，现有的支持向量回归学习算法大多局限于参数选择和大样本学习速度慢。本文考虑采用自适应粒子群优化（APSO）算法来选择支持向量回归模型的参数。为了加速其训练过程，同时在 APSO 迭代的每个参数选择步骤中保持高精度预测，采用最佳训练子集（OTS）方法来选择完整训练数据集的表示数据点。进一步对SVR的最优参数设置和OTS的最优大小进行了初步研究。 UCI数据集和新南威尔士州电力负荷预测的实验结果表明，该模型是有效的并且具有更好的泛化性能。 (C) 2013 Elsevier B.V. 保留所有权利。
Support vector regression (SVR) has become very promising and popular in the field of machine learning due to its attractive features and profound empirical performance for small sample, nonlinearity and high dimensional data application. However, most existing support vector regression learning algorithms are limited to the parameters selection and slow learning for large sample. This paper considers an adaptive particle swarm optimization (APSO) algorithm for the parameters selection of support vector regression model. In order to accelerate its training process while keeping high accurate forecasting in each parameters selection step of APSO iteration, an optimal training subset (OTS) method is carried out to choose the representation data points of the full training data set. Furthermore, the optimal parameters setting of SVR and the optimal size of OTS are studied preliminary. Experimental results of an UCI data set and electric load forecasting in New South Wales show that the proposed model is effective and produces better generalization performance. (C) 2013 Elsevier B. V. All rights reserved.