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

Abstract

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.