基于支持向量数据描述的高效异常数据检测算法

Efficient outlier detection algorithm based on support vector data description

为了解决大规模数据中的异常检测问题,提出了基于支持向量数据描述(SVDD)的高效离群数据检测算法。该算法的核心思想为:首先利用SVDD获得包含单类数据的最小球形边界,然后通过该边界对未知样本数据进行分类,并利用最小闭包球算法对SVDD分类器进行优化求解。在UCI机器学习数据集和入侵检测数据集上将该算法与其他离群数据检测算法进行了实验比较,结果表明,该算法不仅获得了更高的检测准确率,而且具有较低的运行时间。

To efficiently resolve outlier detection problem in large scale data sets,an efficient outlier detection algorithm based on Support Vector Data Description(SVDD) was proposed.The key concept of the algorithm is as follows: First,the spherical boundary containing one-class dataset is obtained via SVDD;then the test datasets are classified with this boundary.In addition,the minimum enclosing ball algorithm is applied to optimize the SVDD detection classifier.The algorithm is compared with other outlier detect...