An improved one-class support vector machine classifier for outlier detection

作者：An Wenjuan; Liang Mangui^*; Liu He

来源：Proceedings of the Institution of Mechanical Engineers - Part C: Journal of Mechanical Engineering Science , 2015, 229(3): 580-588.

DOI：10.1177/0954406214537475

• Outlier detection
• inner-class structure
• within-class scatter
• OC-SVM

Outlier detection, as a type of one-class classification problem, is one of important research topics in data mining and machine learning. Its task is to identify sample points markedly deviating from the normal data. A reliable outlier detector needs to build a model which encloses the normal data tightly. In this paper, an improved one-class SVM (OC-SVM) classifier is proposed for outlier detection problems. We name this method OC-SVM with minimum within-class scatter (OC-WCSSVM), which exploits the inner-class structure of the training set via minimizing the within-class scatter of the training data. This can construct a more accurate hyperplane for outlier detection, such that the margin between the training data and the origin in a higher dimensional space is as large as possible, while at the same time the decision boundary around the normal data is as tight as possible. Experimental results on a synthetic dataset and 10 real-world datasets demonstrate that our proposed OC-WCSSVM algorithm is effective and superior to the compared algorithms.

• 出版日期2015-2
• 单位北京交通大学