Study of One-class Support Vector Machines for Outlier Detection
Outliers are objects that do not comply with the general behavior of the data.In order to detect outliers,One-Class SVM(support vector machine) computes a hyperplane in feature space which has maximal margin to the origin.But the FPR(false positive rate) is always high and the parameters will directly affect the result.So we combine the unsupervised One-class SVM with a supervised method,propose a new model for detecting outliers which make use of ROC(receiver operating characteristic) analysis.The optimum parameters are automatically searched in limited scope,and then lead to the detection decision fucntion after adjusting the threshold.Experiment results show that the method is simple,effective and easy to use.