School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, China.
Department of Electrical Engineering, Lakehead University, Thunder Bay, Ontario, Canada.
PLoS One. 2019 Feb 15;14(2):e0212361. doi: 10.1371/journal.pone.0212361. eCollection 2019.
This paper formulates a support vector machine with quantile hyper-spheres (QHSVM) for pattern classification. The idea of QHSVM is to build two quantile hyper-spheres with the same center for positive or negative training samples. Every quantile hyper-sphere is constructed by using pinball loss instead of hinge loss, which makes the new classification model be insensitive to noise, especially the feature noise around the decision boundary. Moreover, the robustness and generalization of QHSVM are strengthened through maximizing the margin between two quantile hyper-spheres, maximizing the inner-class clustering of samples and optimizing the independent quadratic programming for a target class. Besides that, this paper proposes a novel local center-based density estimation method. Based on it, ρ-QHSVM with surrounding and clustering samples is given. Under the premise of high accuracy, the execution speed of ρ-QHSVM can be adjusted. The experimental results in artificial, benchmark and strip steel surface defects datasets show that the QHSVM model has distinct advantages in accuracy and the ρ-QHSVM model is fit for large-scale datasets.
本文提出了一种基于分位数超球体的支持向量机(QHSVM)用于模式分类。QHSVM 的思想是为正例或负例训练样本构建两个具有相同中心的分位数超球体。每个分位数超球体都是使用弹球损失而不是铰链损失构建的，这使得新的分类模型对噪声不敏感，特别是决策边界附近的特征噪声。此外，通过最大化两个分位数超球体之间的边界、最大化样本的内部聚类以及优化目标类的独立二次规划，增强了 QHSVM 的稳健性和泛化性。此外，本文提出了一种新的基于局部中心的密度估计方法。在此基础上，提出了基于周围样本和聚类样本的ρ-QHSVM。在高精度的前提下，可以调整ρ-QHSVM 的执行速度。在人造、基准和带钢表面缺陷数据集上的实验结果表明，QHSVM 模型在准确性方面具有明显优势，而ρ-QHSVM 模型适用于大规模数据集。
