Overview of Study on One-Class Classifiers
Guiqiang Ni · Dianzi xuebao · 2009
In Traditional binary or multiple classification problem of machine learning,each class of samples is necessary for classifier design;however,in some case only one class of samples can be acquired(due to the complexity or the expensive costs),so we have to learn from the only one class of samples and form the data description for classification.The classification problem is named as one-class classification.Since now there exist the domain-specific and the generic methods,this paper first presents an overview on one-class classification,then emphasizes on the analysis of the kernel-based one-class classifiers and divides this class of methods into two types,that is,dual-based and kernel-induced distance based.Hereafter,the characteristics of these two types of methods are analyzed.Finally,we summarize the implementation techniques and applications of one-class classification in fault analysis,anomaly detection,and disease diagnosis and hostility recognition.