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.