计算机工程2011，Vol.37Issue(6)：157-158,161,3.DOI:10.3969/j.issn.1000-3428.2011.06.054
用于不平衡数据分类的FE-SVDD算法
FE-SVDD Algorithm for Imbalanced Data Classification
摘要
Abstract
It usually exists bias when existing Support Vector Data Description(SVDD) algorithm solves the problem of imbalanced data sets.Aiming at this problem, this paper proposes FE-SVDD algorithm with improved imbalanced data classification. The feature extraction method based on Principal Component Analysis(PCA) is introduced. In this algorithm, the principal values are found respectively of the two classes of samples by using PCA. The penalty is given based on the information provided by the sizes of the two sample data and their values. It verifies the C of SVDD algorithm using artificial data and UCI datasets for the data imbalanced classification problem. Experiment results on artificial data sets and UCI data sets show the method's effectiveness.关键词
模式分类/支持向量数据描述/不平衡数据集/特征提取/主成分分析Key words
pattern classification/ Support Vector Data Description(SVDD)/ imbalanced data sets/ feature extraction/ Principal Component Analysis(PCA)分类
信息技术与安全科学引用本文复制引用
方景龙,王万良,何伟成..用于不平衡数据分类的FE-SVDD算法[J].计算机工程,2011,37(6):157-158,161,3.基金项目
国家自然科学基金资助项目(60874074) （60874074）
浙江省科技计划基金资助重点项目(2009C14032) （2009C14032）
