面向不平衡数据集的线性分类方法研究  

Linear Classification Methods for Imbalanced Datasets

作　　者：殷士勇[1] 

机构地区：[1]盐城纺织职业技术学院,江苏盐城224000

出　　处：《重庆工商大学学报（自然科学版）》2010年第5期467-475,共9页Journal of Chongqing Technology and Business University:Natural Science Edition

摘　　要：近年来,面向不平衡数据集的分类器学习与推广问题越来越受到人们的关注,在此以机器学习数据库、美国邮政编码、2维元音等国际上典型的分类问题为应用背景,重点研究如何用线性分类器解决样本数不平衡的问题;对Fisher、伪逆和单层感知器等3种典型的线性分类器做了深入的研究,并将这3种线性分类方法应用到不平衡数据集的分类中;通过实验及分析,这些新方法对平衡数据集的线性分类起到了良好的分类效果。In recent years,much attention is paid to the learning and generalization problems of classifiers for imbalanced datasets.For the typical classification applications such as machine learning datasets,the US postal service,and 2-dimensional vowels,this paper focuses on the design and learning algorithms of linear classifiers in order to tackle the imbalanced datasets and makes deep studies on Fisher,Pseudo-inverse and single-layer perceptrons and applies these three linear classifiers to imbalanced datasets.Through experiments and analysis,these new methods play a good classification role in linear classification of imbalance datasets.

关 键 词：不平衡数据集 FISHER分类器 伪逆法 单层感知器 线性分类方法 

分 类 号：TP231[自动化与计算机技术—检测技术与自动化装置]