Parameter Optimization of Kernel-based One-class Classifier on Imbalance Learning

Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to learn from unbalanced data set is regarded as the recognition based learning and has shown to have the potential of achieving better performance. Similar to twoclass learning, parameter selection is a significant issue, especially when the classifier is sensitive to the parameters. For one-class learning scheme with the kernel function, such as one-class Support Vector Machine and Support Vector Data Description, besides the parameters involved in the kernel, there is another one-class specific parameter: the rejection rate

• Type: Journal
• Year: 2006
• Where: JCP
• Authors: Ling Zhuang, Honghua Dai
• Keywords: JCP 2006, Learning Scheme, One-class Classification, Parameter