One-class classification can be thought as a special type of two-class classification problem, where data only from one class, the target class, are available for training the classifier (referred to as one-class classifier). The problem of classifying positive (or target) cases in the absence of appropriately characterized negative cases (or outliers) has gained increasing attention in recent years. Several methods are available to solve the one-class classification problem. Three methods are commonly used: density estimation, boundary methods, and reconstruction methods. This paper focuses on boundary methods which include k–center method, nearest neighbor method, one-class support vector machine (OCSVM), and support vector data description (SVDD). In statistical process control (SPC), practitioners successfully used SVDD to detect anomalies or outliers in the process. In this paper, we reformulate the standard OCSVM by a least squares version of the method. This least squares one-class support vector machine (LS-OCSVM) is used to design a control chart for monitoring the mean vector of processes. We compare the performance of the LS-OCSVM chart with the SVDD and $T^2$ chart. The experimental results indicate that the proposed control chart has very good performances.

一类分类可以看作是两类分类问题的一种特殊类型，其中只有来自一类（目标类）的数据可用于训练分类器（称为一类分类器）。近年来，在没有适当表征的阴性病例（或异常值）的情况下，对阳性（或目标）病例进行分类的问题已引起越来越多的关注。有几种方法可以解决一类分类问题。通常使用三种方法：密度估计，边界方法和重构方法。本文着重于边界方法，其中包括k–中心方法，最近邻方法，一类支持向量机（OCSVM）和支持向量数据描述（SVDD）。在统计过程控制（SPC）中，从业者成功地使用SVDD来检测过程中的异常或异常值。在本文中，我们通过该方法的最小二乘法重新制定了标准OCSVM。该最小二乘一类支持向量机（LS-OCSVM）用于设计用于监控过程平均向量的控制图。我们将LS-OCSVM图表的性能与SVDD和${Ť}^2$图表。实验结果表明，所提出的控制图具有很好的性能。