Mu Tingting, Nandi Asoke K
Signal Processing and Communications Research Group, Department of Electrical Engineering and Electronics, University of Liverpool, L693GJ Liverpool, UK.
IEEE Trans Syst Man Cybern B Cybern. 2009 Oct;39(5):1206-16. doi: 10.1109/TSMCB.2009.2013962. Epub 2009 Mar 24.
We propose two variations of the support vector data description (SVDD) with negative samples (NSVDD) that learn a closed spherically shaped boundary around a set of samples in the target class by involving different forms of slack vectors, including the two-norm NSVDD and nu-NSVDD. We extend the NSVDDs to solve the multiclass classification problems based on the distances between the samples and the centers of the learned spherically shaped boundaries in a kernel-defined feature space by using a combination of linear discriminant analysis (LDA) and nearest-neighbor (NN) rule. Extensive simulations are developed with one real-world data set on the automatic monitoring of roller bearings with vibration signals and eight benchmark data sets for both binary and multiclass classification. The benchmark testing results show that our proposed methods provide lower classification error rates and smaller standard deviations with the cross-validation procedure. The two-norm NSVDD with the LDA-NN rule recorded a test accuracy of 100.0% for the binary fault detection of roller bearings and 99.9% for the multiclass classification of roller bearings under six conditions.
我们提出了两种带负样本的支持向量数据描述（SVDD）变体（NSVDD），通过纳入不同形式的松弛向量，在目标类别的一组样本周围学习一个封闭的球形边界，包括二范数NSVDD和nu-NSVDD。我们扩展了NSVDD，通过结合线性判别分析（LDA）和最近邻（NN）规则，基于内核定义特征空间中样本与学习到的球形边界中心之间的距离来解决多类分类问题。利用一个关于振动信号自动监测滚动轴承的真实数据集以及八个用于二分类和多类分类的基准数据集进行了广泛的模拟。基准测试结果表明，我们提出的方法在交叉验证过程中提供了更低的分类错误率和更小的标准差。采用LDA-NN规则的二范数NSVDD在滚动轴承的二分类故障检测中测试准确率达到100.0%，在六种条件下滚动轴承的多类分类中测试准确率达到99.9%。
