A novel virtual sample generation method based on Gaussian distribution
A novel virtual sample generation method based on Gaussian distribution
复制标题
一种基于高斯分布的虚拟样本生成新方法
DOI：
10.1016/j.knosys.2010.12.010
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发表时间：
2011-08-01
影响因子：
8.8
通讯作者：
Zhang, Jian-Pei
中科院分区：
文献类型：
--
作者：
Yang, Jing;Yu, Xu;Zhang, Jian-Pei
传统的机器学习算法在有噪声、不平衡和小样本训练集上泛化能力不理想。本文提出了一种基于高斯分布的虚拟样本生成方法。该方法首先确定高斯分布的均值和标准误差。然后，可以利用这种高斯分布生成虚拟样本。最后，将虚拟样本加入到原训练集中，构造新的训练集。这项工作表明，在新训练集上的训练相当于小样本问题的一种正则化形式，或不平衡样本问题的成本敏感学习。实验表明，在给定适当数量的虚拟样本重复的情况下，分类器在新训练集上的泛化能力优于在原始训练集上的泛化能力。(C) 2011年Elsevier B.V.出版
Traditional machine learning algorithms are not with satisfying generalization ability on noisy, imbalanced, and small sample training set. In this work, a novel virtual sample generation (VSG) method based on Gaussian distribution is proposed. Firstly, the method determines the mean and the standard error of Gaussian distribution. Then, virtual samples can be generated by such Gaussian distribution. Finally, a new training set is constructed by adding the virtual samples to the original training set. This work has shown that training on the new training set is equivalent to a form of regularization regarding small sample problems, or cost-sensitive learning regarding imbalanced sample problems. Experiments show that given a suitable number of virtual sample replicates, the generalization ability of the classifiers on the new training sets can be better than that on the original training sets. (C) 2011 Published by Elsevier B.V.