期刊
KNOWLEDGE-BASED SYSTEMS
卷 59, 期 -, 页码 75-84出版社
ELSEVIER
DOI: 10.1016/j.knosys.2014.01.020
关键词
One-class classification; OCSVM; Gaussian kernel; Parameter selection; Fault detection
资金
- National Key Technologies Research and Development Program of China [2011AA060203]
- No 2 National Science and Technology Specific Project of China [2011ZX02504]
As one of the methods to solve one-class classification problems (OCC), one-class support vector machines (OCSVM) have been applied to fault detection in recent years. Among all the kernels available for OCSVM, the Gaussian kernel is the most commonly used one. The selection of Gaussian kernel parameters influences greatly the performances of classifiers, which remains as an open problem. In this paper two methods are proposed to select Gaussian kernel parameters in OCSVM: according to the first one, the parameters are selected using the information of the farthest and the nearest neighbors of each sample; using the second one, the parameters are determined via detecting the tightness of the decision boundaries. The two proposed methods are tested on UCI data sets and Tennessee Eastman Process benchmark data sets. The results show that, the two proposed methods can be used to select suitable parameters for the Gaussian kernel, enabling the resulting OCSVM models to perform well on fault detection. (C) 2014 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据