摘要: 分析当前的主要预警方法,指出由于缺少非正常数据样本,使得现有的大部分预警方法不适用。为解决该问题,提出了基于核方法的支持向量数据描述预警技术。建立了一个用于检测非正常数据对象的一类分类器,检测数据对象是否在正常值超球体范围内。如果在超球体外,预警专家将最终确认这个数据对象是否为非正常的预警警兆。以广东省江门市的宏观区域经济数据为例,证明了该预警技术的有效性。
关键词:
预警,
支持向量数据描述,
核方法,
数据样本
Abstract: After reviewing the current early warning researches, this paper presents that most of current early warning methods are unsuitable because of lacking a historical “ill-represented” dataset. And then the support vector data description early warning technique based on kernel method is proposed to solve the problem. A one-class classifier is fitted to detect the “ill-represented” data objects by enclosing all “good” data objects in a hypersphere. If an object is outside the boundary of the hypersphere, an early warning expert would be prompted to decide whether the object is enough “ill-represented” for issuing a warning. An early warning experiment based on the macro-economic dataset of Jiangmen, Guangdong is conducted to verify the proposed technique.
Key words:
Early warning,
Support vector data description (SVDD),
Kernel method,
Data feature
中图分类号:
林 健;彭敏晶;. 基于支持向量数据描述的预警技术及其应用[J]. 计算机工程, 2006, 32(20): 7-9,12.
LIN Jian; PENG Minjing;. SVDD Based Early Warning Technique and Its Application[J]. Computer Engineering, 2006, 32(20): 7-9,12.