电子学报2017，Vol.45Issue(4)：813-819,7.DOI:10.3969/j.issn.0372-2112
一种基于单簇核PCM的SVDD离群点检测方法
A One-Cluster Kernel PCM Based SVDD Method for Outlier Detection
摘要
Abstract
In order to reduce the negative influence of outliers on the model of support vector data description (SVDD) when the training dataset contains both normal samples and outliers which are all labeled as target class,a one-cluster kernel possibilistic C-means based SVDD method for outlier detection is proposed.In this paper,each sample of the training dataset is assigned a confidence level based on the membership degree of each sample belonging to the normal class,which is obtained through the one-cluster kernel PCM clustering.The proposed algorithm incorporates the confidence levels into the training model to reduce the importance of the samples which have less confidence levels.The experimental results show that the proposal significantly improves the effect of outlier detection,compared with the existing SVDD-based outlier detection methods.关键词
离群点检测/支持向量数据描述/可能性C-均值/置信度Key words
outlier detection/support vector data description分类
信息技术与安全科学引用本文复制引用
杨金鸿,邓廷权..一种基于单簇核PCM的SVDD离群点检测方法[J].电子学报,2017,45(4):813-819,7.基金项目
国家自然科学基金(No.11471001) （No.11471001）
