节点文献
基于相对密度的支持向量数据描述算法研究
Research on Support Vector Data Description Based on Relative Density Degree
【作者】 张海飞；
【导师】 张莉；
【作者基本信息】 苏州大学 ， 计算机技术（专业学位）， 2014， 硕士
【摘要】 异常检测问题普遍存在于现代医学研究、新型农业研究以及机械工程安全中。支持向量数据描述(Support Vector Data Description，SVDD)算法是一种异常检测方法，该方法需要建立一个球体去尽可能多地包含所有已知的正常样本。在处理异常检测问题中，根据数据的特性可分为两种情况：一种情况是只包含正常样本；另一种情况是除了正常的样本，还包含小数量级的异常样本。Lee等人在SVDD算法中引入了样本相对密度的概念，改善了SVDD算法的性能。本文在样本相对密度算法和SVDD算法基础上做了如下进一步的研究：(1)在密度诱导支持向量数据描述(Density-induced Support Vector DataDescription，D-SVDD)算法基础上，文中详细考虑了参数T的取值对算法模型分类性能和算法稳定性的影响，并给出了参数T的取值区间。(2)将异常样本信息加入到密度惩罚支持向量数据描述(Density-Punished SupportVector Data Descripetion，DP-SVDD)算法的训练过程中，使得新算法能处理带有异常样本的情形，从而，能够获得更高的稳定性和识别精度。(3)在ODP-SVDD算法的基础上，引进模糊隶属度函数，减弱了有噪正常样本对模型性能的影响，有效地提高了算法的性能。
【Abstract】 Novelty detection is a common problem which exists in modern medical research, newagricultural research and engineering security. Support vector data description (SVDD) is amethod for novelty detection. SVDD tries to establish a hypersphere and include all theknown normal samples. About detection problems, there are two kinds of data or the datawith only normal samples and the data with both normal and abnormal samples. Note thatthe number of abnormal samples is very small even if the data contains abnormal samples.Lee et al. introduced the concept of the relative density degree of samples, which canimprove the traditional SVDD algorithm. Based on the relative density degree and SVDD,this thesis has the following contributions.(1) Based on the Density-induced Support Vector Data Description (D-SVDD)algorithm, this thesis analyzes the parameter T and its influence on the performance ofalgorithm. We present a method for selecting the value of parameter T.(2) This thesis puts the abnormal sample information into the training process ofDensity-punished Support Vector Data Description (DP-SVDD) algorithm, and obtains amore stable and better result.(3) In this thesis, we introduce fuzzy membership function into ODP-SVDD to reducethe effect of noise normal points on the model. The new method effectively improves theability of ODP-SVDD.