基于径向基支持向量机的异常血样监测方法

Detection of Abnormal Blood Samples Using Support Vector Machines with Radial Basis Function

本文提出了一种基于支持向量机 (SVM)的异常血样监测方法 .异常血样监测可以归结为非对称的非线性分类问题 ,即血样训练样本不对称和分类边界的裕度不对称 .本文在血样向量空间上虚拟了异常血样超球面 ,利用高斯径向基 (RBF)核函数对血样向量升维使之在高维内积空间中线性可分 .通过调节高斯径向基的宽度和边界裕度 ,可以确定紧包正常血样特征向量子空间且具有最佳监测效果的分类边界 .该方法在试验数据上获得了误警率 3.1 9%和漏警率6 .38%、准确率 90 .4

This paper describes an approach for the detection of abnormal blood samples using Support Vector Machines(SVM). The problem of abnormality detection falls in more general category of non linear binary classification, but it is with the remarkable property that the training samples are much imbalanced and the margins of the classifying boundary to each class are expected to be unequal. Considering the particularity, we suppose some abnormal blood samples on a hypersphere. These supposed abnormal blood training samples, together with the practical normal blood training samples, are mapped into a high dimensional inner product space by Gaussian Radial Basis Function(RBF) kernel, where they can be separated by a linear hyperplane. Through adapting the width of RBF and the margin of separating hyperplane, the boundary that surrounds the subspace of normal blood samples closely and is with the best result of abnormality detection can be determined. This approach achieved the good result that the false alarm rate is 3.19%, the missing alarm rate is 6.38% and the accuracy rate is 90.43%.